@article{Vazquez2017,

title = {A Robust Scheme for Distributed Particle Filtering in Wireless Sensors Networks},

author = {Manuel A V\'{a}zquez and Joaqu\'{i}n M\'{i}guez},

url = {http://www.sciencedirect.com/science/article/pii/S016516841630189X},

doi = {10.1016/j.sigpro.2016.08.003},

issn = {01651684},

year  = {2017},

date = {2017-02-01},

journal = {Signal Processing},

volume = {131},

pages = {190--201},

abstract = {Wireless sensor networks (WSNs) have become a popular technology for a broad range of applications where the goal is to track and forecast the evolution of time-varying physical magnitudes. Several authors have investigated the use of particle filters (PFs) in this scenario. PFs are very flexible, Monte Carlo based algorithms for tracking and prediction in state-space dynamical models. However, to implement a PF in a WSN, the algorithm should run over different nodes in the network to produce estimators based on locally collected data. These local estimators then need to be combined so as to produce a global estimator. Existing approaches to the problem are either heuristic or well-principled but impractical (as they impose stringent conditions on the WSN communication capacity). Here, we introduce a novel distributed PF that relies on the computation of median posterior probability distributions in order to combine local Bayesian estimators (obtained at different nodes) in a way that is efficient, both computation and communication-wise. An extensive simulation study for a target tracking problem shows that the proposed scheme is competitive with existing consensus-based distributed PFs in terms of estimation accuracy, while it clearly outperforms these methods in terms of robustness and communication requirements.},

keywords = {Distributed particle filtering (DPF), Journal, Median posterior, Robust statistics, Sequential Monte Carlo Methods (SMC), Wireless sensors networks (WSNs)},

pubstate = {published},

tppubtype = {article}

}

@article{Elvira2017,

title = {Improving Population Monte Carlo: Alternative Weighting and Resampling Schemes},

author = {Victor Elvira and Luca Martino and David Luengo and Monica F Bugallo},

url = {http://www.sciencedirect.com/science/article/pii/S0165168416301633},

doi = {10.1016/j.sigpro.2016.07.012},

issn = {01651684},

year  = {2017},

date = {2017-02-01},

journal = {Signal Processing},

volume = {131},

pages = {77--91},

abstract = {Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in nature: at each step they generate samples from a proposal distribution and assign them weights according to the importance sampling principle. Critical issues in applying PMC methods are the choice of the generating functions for the samples and the avoidance of the sample degeneracy. In this paper, we propose three new schemes that considerably improve the performance of the original PMC formulation by allowing for better exploration of the space of unknowns and by selecting more adequately the surviving samples. A theoretical analysis is performed, proving the superiority of the novel schemes in terms of variance of the associated estimators and preservation of the sample diversity. Furthermore, we show that they outperform other state of the art algorithms (both in terms of mean square error and robustness w.r.t. initialization) through extensive numerical simulations.},

keywords = {Adaptive importance sampling, Journal, population Monte Carlo, Proposal distribution, Resampling},

pubstate = {published},

tppubtype = {article}

}

@article{Yiu2017,

title = {Wireless RSSI Fingerprinting Localization},

author = {Simon Yiu and Marzieh Dashti and Holger Claussen and Fernando Perez-Cruz},

doi = {10.1016/j.sigpro.2016.07.005},

issn = {01651684},

year  = {2017},

date = {2017-02-01},

journal = {Signal Processing},

volume = {131},

pages = {235--244},

abstract = {Localization has attracted a lot of research effort in the last decade due to the explosion of location based service (LBS). In particular, wireless fingerprinting localization has received much attention due to its simplicity and compatibility with existing hardware. In this work, we take a closer look at the underlying aspects of wireless fingerprinting localization. First, we review the various methods to create a radiomap. In particular, we look at the traditional fingerprinting method which is based purely on measurements, the parametric pathloss regression model and the non-parametric Gaussian Process (GP) regression model. Then, based on these three methods and measurements from a real world deployment, the various aspects such as the density of access points (APs) and impact of an outdated signature map which affect the performance of fingerprinting localization are examined. At the end of the paper, the audiences should have a better understanding of what to expect from fingerprinting localization in a real world deployment.},

keywords = {Fingerprinting localization, Gaussian Process, Journal, Location-based service (LBS), Machine learning, Non-parametric model, Pathloss model, Received signal strength indicator (RSSI)},

pubstate = {published},

tppubtype = {article}

}

@article{Santos2017,

title = {Expectation Propagation as Turbo Equalizer in ISI Channels},

author = {Irene Santos and Juan Jose Murillo-Fuentes and Rafael Boloix-Tortosa and Eva Arias-de-Reyna and Pablo M Olmos},

url = {http://ieeexplore.ieee.org/document/7587428/},

doi = {10.1109/TCOMM.2016.2616141},

issn = {0090-6778},

year  = {2017},

date = {2017-01-01},

journal = {IEEE Transactions on Communications},

volume = {65},

number = {1},

pages = {360--370},

abstract = {In probabilistic equalization of channels with inter-symbol interference, the BCJR algorithm and its approximations become intractable for high-order modulations, even for moderate channel dispersions. In this paper, we introduce a novel soft equalizer to approximate the symbol a posteriori probabilities (APP), where the expectation propagation (EP) algorithm is used to provide an accurate estimation. This new soft equalizer is presented as a block solution, denoted as block-EP (BEP), where the structure of the matrices involved is exploited to reduce the complexity order to O(LN2) , i.e., linear in the length of the channel, L , and quadratic in the frame length, N . The solution is presented in complex-valued formulation within a turbo equalization scheme. This algorithm can be cast as a linear minimum-mean-squared-error (LMMSE) turbo equalization with double feedback architecture, where constellations being discrete is a restriction exploited by the EP that provides a first refinement of the APP. In the experiments included, the BEP exhibits a robust performance, regardless of the channel response, with gains in the range 1.5\textendash5 dB compared with the LMMSE equalization.},

keywords = {BCJR, complex-valued, Expectation propagation (EP), ISI, Journal, turbo equalization},

pubstate = {published},

tppubtype = {article}

}

@article{Martino2017,

title = {Cooperative Parallel Particle Filters for Online Model Selection and Applications to Urban Mobility},

author = {Luca Martino and Jesse Read and Victor Elvira and Francisco Louzada},

url = {http://www.sciencedirect.com/science/article/pii/S1051200416301610},

doi = {10.1016/j.dsp.2016.09.011},

issn = {10512004},

year  = {2017},

date = {2017-01-01},

journal = {Digital Signal Processing},

volume = {60},

pages = {172--185},

abstract = {We design a sequential Monte Carlo scheme for the dual purpose of Bayesian inference and model selection. We consider the application context of urban mobility, where several modalities of transport and different measurement devices can be employed. Therefore, we address the joint problem of online tracking and detection of the current modality. For this purpose, we use interacting parallel particle filters, each one addressing a different model. They cooperate for providing a global estimator of the variable of interest and, at the same time, an approximation of the posterior density of each model given the data. The interaction occurs by a parsimonious distribution of the computational effort, with online adaptation for the number of particles of each filter according to the posterior probability of the corresponding model. The resulting scheme is simple and flexible. We have tested the novel technique in different numerical experiments with artificial and real data, which confirm the robustness of the proposed scheme.},

keywords = {Distributed inference, Journal, Marginal likelihood estimation, Modality detection, Parallel particle filters, Sequential model selection, Urban mobility},

pubstate = {published},

tppubtype = {article}

}

@article{Pradier2016,

title = {Entropy-Constrained Scalar Quantization with a Lossy-Compressed Bit},

author = {Melanie F. Pradier and Pablo M Olmos and Fernando Perez-Cruz},

url = {http://www.mdpi.com/1099-4300/18/12/449},

doi = {10.3390/e18120449},

issn = {1099-4300},

year  = {2016},

date = {2016-12-01},

journal = {Entropy},

volume = {18},

number = {12},

pages = {449},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {We consider the compression of a continuous real-valued source X using scalar quantizers and average squared error distortion D. Using lossless compression of the quantizer's output, Gish and Pierce showed that uniform quantizing yields the smallest output entropy in the limit D → 0 , resulting in a rate penalty of 0.255 bits/sample above the Shannon Lower Bound (SLB). We present a scalar quantization scheme named lossy-bit entropy-constrained scalar quantization (Lb-ECSQ) that is able to reduce the D → 0 gap to SLB to 0.251 bits/sample by combining both lossless and binary lossy compression of the quantizer's output. We also study the low-resolution regime and show that Lb-ECSQ significantly outperforms ECSQ in the case of 1-bit quantization.},

keywords = {Journal, scalar quantization, source coding},

pubstate = {published},

tppubtype = {article}

}

@article{Vazquez2016,

title = {On the Use of the Channel Second-Order Statistics in MMSE Receivers for Time- and Frequency-Selective MIMO Transmission Systems},

author = {Manuel A V\'{a}zquez and Joaqu\'{i}n M\'{i}guez},

url = {http://jwcn.eurasipjournals.springeropen.com/articles/10.1186/s13638-016-0768-0},

doi = {10.1186/s13638-016-0768-0},

year  = {2016},

date = {2016-12-01},

journal = {EURASIP Journal on Wireless Communications and Networking},

volume = {2016},

number = {1},

publisher = {Springer International Publishing},

abstract = {Equalization of unknown frequency- and time-selective multiple input multiple output (MIMO) channels is often carried out by means of decision feedback receivers. These consist of a channel estimator and a linear filter (for the estimation of the transmitted symbols), interconnected by a feedback loop through a symbol-wise threshold detector. The linear filter is often a minimum mean square error (MMSE) filter, and its mathematical expression involves second-order statistics (SOS) of the channel, which are usually ignored by simply assuming that the channel is a known (deterministic) parameter given by an estimate thereof. This appears to be suboptimal and in this work we investigate the kind of performance gains that can be expected when the MMSE equalizer is obtained using SOS of the channel process. As a result, we demonstrate that improvements of several dBs in the signal-to-noise ratio needed to achieve a prescribed symbol error rate are possible.},

keywords = {data estimation, Joint channel, Journal, MIMO, MMSE, Second-order statistics},

pubstate = {published},

tppubtype = {article}

}

@article{Martino2016b,

title = {Orthogonal Parallel MCMC Methods for Sampling and Optimization},

author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander and Francisco Louzada},

url = {http://www.sciencedirect.com/science/article/pii/S1051200416300987},

doi = {10.1016/j.dsp.2016.07.013},

issn = {10512004},

year  = {2016},

date = {2016-11-01},

journal = {Digital Signal Processing},

volume = {58},

pages = {64--84},

abstract = {Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, several schemes employing multiple parallel MCMC chains have been recently introduced. In this work, we describe a novel parallel interacting MCMC scheme, called orthogonal MCMC (O-MCMC), where a set of “vertical” parallel MCMC chains share information using some “horizontal” MCMC techniques working on the entire population of current states. More specifically, the vertical chains are led by random-walk proposals, whereas the horizontal MCMC techniques employ independent proposals, thus allowing an efficient combination of global exploration and local approximation. The interaction is contained in these horizontal iterations. Within the analysis of different implementations of O-MCMC, novel schemes in order to reduce the overall computational cost of parallel multiple try Metropolis (MTM) chains are also presented. Furthermore, a modified version of O-MCMC for optimization is provided by considering parallel simulated annealing (SA) algorithms. Numerical results show the advantages of the proposed sampling scheme in terms of efficiency in the estimation, as well as robustness in terms of independence with respect to initial values and the choice of the parameters.},

keywords = {Bayesian inference, Block Independent Metropolis, Journal, Optimization, Parallel Markov Chain Monte Carlo, Parallel Multiple Try Metropolis, Parallel Simulated Annealing, Recycling samples},

pubstate = {published},

tppubtype = {article}

}

@article{Koch2016b,

title = {The Shannon Lower Bound Is Asymptotically Tight},

author = {Tobias Koch},

url = {http://ieeexplore.ieee.org/document/7556344/},

doi = {10.1109/TIT.2016.2604254},

issn = {0018-9448},

year  = {2016},

date = {2016-11-01},

journal = {IEEE Transactions on Information Theory},

volume = {62},

number = {11},

pages = {6155--6161},

abstract = {The Shannon lower bound is one of the few lower bounds on the rate-distortion function that holds for a large class of sources. In this paper, which considers exclusively norm-based difference distortion measures, it is demonstrated that its gap to the rate-distortion function vanishes as the allowed distortion tends to zero for all sources having finite differential entropy and whose integer part has finite entropy. Conversely, it is demonstrated that if the integer part of the source has infinite entropy, then its rate-distortion function is infinite for every finite distortion level. Thus, the Shannon lower bound provides an asymptotically tight bound on the rate-distortion function if, and only if, the integer part of the source has finite entropy.},

keywords = {Journal, R{\'{e}}nyi information dimension, Rate-distortion theory, Shannon lower bound},

pubstate = {published},

tppubtype = {article}

}

@article{Song2016,

title = {Canonical Correlation Analysis of High-Dimensional Data With Very Small Sample Support},

author = {Yang Song and Peter J Schreier and David Ram\'{i}rez and Tanuj Hasija},

url = {http://www.sciencedirect.com/science/article/pii/S0165168416300834},

doi = {10.1016/j.sigpro.2016.05.020},

issn = {01651684},

year  = {2016},

date = {2016-11-01},

journal = {Signal Processing},

volume = {128},

pages = {449--458},

abstract = {This paper is concerned with the analysis of correlation between two high-dimensional data sets when there are only few correlated signal components but the number of samples is very small, possibly much smaller than the dimensions of the data. In such a scenario, a principal component analysis (PCA) rank-reduction preprocessing step is commonly performed before applying canonical correlation analysis (CCA). We present simple, yet very effective, approaches to the joint model-order selection of the number of dimensions that should be retained through the PCA step and the number of correlated signals. These approaches are based on reduced-rank versions of the Bartlett\textendashLawley hypothesis test and the minimum description length information-theoretic criterion. Simulation results show that the techniques perform well for very small sample sizes even in colored noise.},

keywords = {Bartlett-Lawley statistic, Canonical correlation analysis, Journal, Model-order selection, Principal component analysis, Small sample support},

pubstate = {published},

tppubtype = {article}

}

@article{Bocharova2016,

title = {Multi-Class Source-Channel Coding},

author = {Irina E Bocharova and Albert Guill\'{e}n i F\`{a}bregas and Boris D Kudryashov and Alfonso Martinez and Adria Tauste Campo and Gonzalo Vazquez-Vilar},

url = {http://arxiv.org/abs/1410.8714},

year  = {2016},

date = {2016-09-01},

journal = {IEEE Transactions on Information Theory},

volume = {62},

number = {9},

pages = {5093 -- 5104},

abstract = {This paper studies an almost-lossless source-channel coding scheme in which source messages are assigned to different classes and encoded with a channel code that depends on the class index. The code performance is analyzed by means of random-coding error exponents and validated by simulation of a low-complexity implementation using existing source and channel codes. While each class code can be seen as a concatenation of a source code and a channel code, the overall performance improves on that of separate source-channel coding and approaches that of joint source-channel coding when the number of classes increases.},

keywords = {Channel Coding, Complexity theory, error probability, Indexes, Journal, Maximum likelihood decoding},

pubstate = {published},

tppubtype = {article}

}

@article{Valera2016b,

title = {Infinite Factorial Unbounded-State Hidden Markov Model},

author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},

url = {http://www.ncbi.nlm.nih.gov/pubmed/26571511 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true\&arnumber=7322279},

doi = {10.1109/TPAMI.2015.2498931},

issn = {1939-3539},

year  = {2016},

date = {2016-09-01},

journal = {IEEE transactions on pattern analysis and machine intelligence},

volume = {38},

number = {9},

pages = {1816 -- 1828},

abstract = {There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or the number of states of the FHMM cannot be known or limited a priori. In this paper, we propose an infinite factorial unbounded-state hidden Markov model (IFUHMM), in which the number of parallel hidden Markov models (HMMs) and states in each HMM are potentially unbounded. We rely on a Bayesian nonparametric (BNP) prior over integer-valued matrices, in which the columns represent the Markov chains, the rows the time indexes, and the integers the state for each chain and time instant. First, we extend the existent infinite factorial binary-state HMM to allow for any number of states. Then, we modify this model to allow for an unbounded number of states and derive an MCMC-based inference algorithm that properly deals with the trade-off between the unbounded number of states and chains. We illustrate the performance of our proposed models in the power disaggregation problem.},

keywords = {Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Journal, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium},

pubstate = {published},

tppubtype = {article}

}

@article{Nazabal2016b,

title = {Human Activity Recognition by Combining a Small Number of Classifiers.},

author = {Alfredo Naz\'{a}bal and Pablo Garcia-Moreno and Antonio Art\'{e}s-Rodr\'{i}guez and Zoubin Ghahramani},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7161292},

doi = {10.1109/JBHI.2015.2458274},

issn = {2168-2208},

year  = {2016},

date = {2016-09-01},

journal = {IEEE journal of biomedical and health informatics},

volume = {20},

number = {5},

pages = {1342 -- 1351},

publisher = {IEEE},

abstract = {We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.},

keywords = {Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Journal, Sensor systems},

pubstate = {published},

tppubtype = {article}

}

@article{Durisi2016a,

title = {Towards Massive, Ultra-Reliable, and Low-Latency Wireless Communication with Short Packets},

author = {Giuseppe Durisi and Tobias Koch and Petar Popovski},

url = {http://arxiv.org/abs/1504.06526},

year  = {2016},

date = {2016-09-01},

journal = {Proceedings of the IEEE},

volume = {104},

number = {9},

pages = {1711 -- 1726},

abstract = {Most of the recent advances in the design of high-speed wireless systems are based on information-theoretic principles that demonstrate how to efficiently transmit long data packets. However, the upcoming wireless systems, notably the 5G system, will need to support novel traffic types that use short packets. For example, short packets represent the most common form of traffic generated by sensors and other devices involved in Machine-to-Machine (M2M) communications. Furthermore, there are emerging applications in which small packets are expected to carry critical information that should be received with low latency and ultra-high reliability. Current wireless systems are not designed to support short-packet transmissions. For example, the design of current systems relies on the assumption that the metadata (control information) is of negligible size compared to the actual information payload. Hence, transmitting metadata using heuristic methods does not affect the overall system performance. However, when the packets are short, metadata may be of the same size as the payload, and the conventional methods to transmit it may be highly suboptimal. In this article, we review recent advances in information theory, which provide the theoretical principles that govern the transmission of short packets. We then apply these principles to three exemplary scenarios (the two-way channel, the downlink broadcast channel, and the uplink random access channel), thereby illustrating how the transmission of control information can be optimized when the packets are short. The insights brought by these examples suggest that new principles are needed for the design of wireless protocols supporting short packets. These principles will have a direct impact on the system design.},

keywords = {finite blocklength, Journal, massive M2M communication, short packets, ultrareliable communication (URC), Wireless 5G systems},

pubstate = {published},

tppubtype = {article}

}

@article{Vazquez-Vilar2016,

title = {Bayesian M-Ary Hypothesis Testing: The Meta-Converse and Verd\'{u}-Han Bounds Are Tight},

author = {Gonzalo Vazquez-Vilar and Adria Tauste Campo and Albert Guillen i Fabregas and Alfonso Martinez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7434042},

doi = {10.1109/TIT.2016.2542080},

issn = {0018-9448},

year  = {2016},

date = {2016-05-01},

journal = {IEEE Transactions on Information Theory},

volume = {62},

number = {5},

pages = {2324--2333},

abstract = {Two alternative exact characterizations of the minimum error probability of Bayesian M-ary hypothesis testing are derived. The first expression corresponds to the error probability of an induced binary hypothesis test and implies the tightness of the meta-converse bound by Polyanskiy et al.; the second expression is a function of an information-spectrum measure and implies the tightness of a generalized Verd\'{u}-Han lower bound. The formulas characterize the minimum error probability of several problems in information theory and help to identify the steps where existing converse bounds are loose.},

keywords = {Bayes methods, Channel Coding, Electronic mail, error probability, Journal, Random variables, Testing},

pubstate = {published},

tppubtype = {article}

}

@article{Miguez2016,

title = {A Proof of Uniform Convergence Over Time for a Distributed Particle Filter},

author = {Joaqu\'{i}n M\'{i}guez and Manuel A V\'{a}zquez},

url = {http://www.sciencedirect.com/science/article/pii/S0165168415004077},

doi = {10.1016/j.sigpro.2015.11.015},

issn = {01651684},

year  = {2016},

date = {2016-05-01},

journal = {Signal Processing},

volume = {122},

pages = {152--163},

abstract = {Distributed signal processing algorithms have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters (PFs). However, most distributed PFs involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard PFs do not hold for their distributed counterparts. In this paper, we analyze a distributed PF based on the non-proportional weight-allocation scheme of Bolic et al (2005) and prove rigorously that, under certain stability assumptions, its asymptotic convergence is guaranteed uniformly over time, in such a way that approximation errors can be kept bounded with a fixed computational budget. To illustrate the theoretical findings, we carry out computer simulations for a target tracking problem. The numerical results show that the distributed PF has a negligible performance loss (compared to a centralized filter) for this problem and enable us to empirically validate the key assumptions of the analysis.},

keywords = {Convergence analysis, Distributed algorithms, Journal, Parallelization, Particle filtering, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {article}

}

@article{Martin-Fernandez2015b,

title = {A Bayesian Method for Model Selection in Environmental Noise Prediction},

author = {L Mart\'{i}n-Fern\'{a}ndez and D P Ruiz and A J Torija and Joaquin Miguez},

url = {http://www.researchgate.net/publication/268213140_A_Bayesian_method_for_model_selection_in_environmental_noise_prediction},

issn = {1726-2135},

year  = {2016},

date = {2016-03-01},

journal = {Journal of Environmental Informatics},

volume = {27},

number = {1},

pages = {31--42},

abstract = {Environmental noise prediction and modeling are key factors for addressing a proper planning and management of urban sound environments. In this paper we propose a maximum a posteriori (MAP) method to compare nonlinear state-space models that describe the problem of predicting environmental sound levels. The numerical implementation of this method is based on particle filtering and we use a Markov chain Monte Carlo technique to improve the resampling step. In order to demonstrate the validity of the proposed approach for this particular problem, we have conducted a set of experiments where two prediction models are quantitatively compared using real noise measurement data collected in different urban areas.},

keywords = {Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Martino2016a,

title = {Layered adaptive importance sampling},

author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander},

url = {http://link.springer.com/10.1007/s11222-016-9642-5},

doi = {10.1007/s11222-016-9642-5},

issn = {0960-3174},

year  = {2016},

date = {2016-03-01},

journal = {Statistics and Computing},

pages = {1--25},

publisher = {Springer US},

abstract = {Monte Carlo methods represent the de facto standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples. The performance of any such method is strictly related to the specification of the proposal distribution, such that unfortunate choices easily wreak havoc on the resulting estimators. In this work, we introduce a layered (i.e., hierarchical) procedure to generate samples employed within a Monte Carlo scheme. This approach ensures that an appropriate equivalent proposal density is always obtained automatically (thus eliminating the risk of a catastrophic performance), although at the expense of a moderate increase in the complexity. Furthermore, we provide a general unified importance sampling (IS) framework, where multiple proposal densities are employed and several IS schemes are introduced by applying the so-called deterministic mixture approach. Finally, given these schemes, we also propose a novel class of adaptive importance samplers using a population of proposals, where the adaptation is driven by independent parallel or interacting Markov chain Monte Carlo (MCMC) chains. The resulting algorithms efficiently combine the benefits of both IS and MCMC methods.},

keywords = {Bayesian inference Adaptive importance sampling Po, Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Durisi2016b,

title = {Short-Packet Communications Over Multiple-Antenna Rayleigh-Fading Channels},

author = {Giuseppe Durisi and Tobias Koch and Johan Ostman and Yury Polyanskiy and Wei Yang},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7362178},

doi = {10.1109/TCOMM.2015.2511087},

issn = {0090-6778},

year  = {2016},

date = {2016-02-01},

journal = {IEEE Transactions on Communications},

volume = {64},

number = {2},

pages = {618--629},

publisher = {IEEE},

abstract = {Motivated by the current interest in ultra-reliable, low-latency, machine-type communication systems, we investigate the tradeoff between reliability, throughput, and latency in the transmission of information over multiple-antenna Rayleigh block-fading channels. Specifically, we obtain finite-blocklength, finite-SNR upper and lower bounds on the maximum coding rate achievable over such channels for a given constraint on the packet error probability. Numerical evidence suggests that our bounds delimit tightly the maximum coding rate already for short blocklengths (packets of about 100 symbols). Furthermore, our bounds reveal the existence of a tradeoff between the rate gain obtainable by spreading each codeword over all available time-frequency-spatial degrees of freedom, and the rate loss caused by the need of estimating the fading coefficients over these degrees of freedom. In particular, our bounds allow us to determine the optimal number of transmit antennas and the optimal number of time-frequency diversity branches that maximize the rate. Finally, we show that infinite-blocklength performance metrics such as the ergodic capacity and the outage capacity yield inaccurate throughput estimates},

keywords = {diversity branches, Encoding, ergodic capacity, Fading, fading channels, finite-blocklength information theory, finiteblocklength information theory, infinite-blocklength performance metrics, Journal, machine-type communication systems, maximum coding rate, Mission critical systems, mission-critical machine-type communications, multiple antennas, multiple-antenna Rayleigh block-fading channels, Multiplexing, optimal number, outage capacity, rate gain, Rayleigh channels, Receivers, Reliability, short-packet communications, spatial multiplexing, Throughput, Time-frequency analysis, time-frequency-spatial degrees of freedom, transmit antennas, transmit diversity, Transmitting antennas, Ultra-reliable low-latency communications},

pubstate = {published},

tppubtype = {article}

}

@article{Valera2016ab,

title = {Infinite Continuous Feature Model for Psychiatric Comorbidity Analysis.},

author = {Isabel Valera and Francisco J R Ruiz and Pablo M Olmos and Carlos Blanco and Fernando Perez-Cruz},

url = {http://www.ncbi.nlm.nih.gov/pubmed/26654208},

doi = {10.1162/NECO_a_00805},

issn = {1530-888X},

year  = {2016},

date = {2016-02-01},

journal = {Neural computation},

volume = {28},

number = {2},

pages = {354--381},

abstract = {We aim at finding the comorbidity patterns of substance abuse, mood and personality disorders using the diagnoses from the National Epidemiologic Survey on Alcohol and Related Conditions database. To this end, we propose a novel Bayesian nonparametric latent feature model for categorical observations, based on the Indian buffet process, in which the latent variables can take values between 0 and 1. The proposed model has several interesting features for modeling psychiatric disorders. First, the latent features might be off, which allows distinguishing between the subjects who suffer a condition and those who do not. Second, the active latent features take positive values, which allows modeling the extent to which the patient has that condition. We also develop a new Markov chain Monte Carlo inference algorithm for our model that makes use of a nested expectation propagation procedure.},

keywords = {Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Borchani2016,

title = {Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers},

author = {Hanen Borchani and Pedro Larra\~{n}aga and J Gama and Concha Bielza},

url = {http://cig.fi.upm.es/node/879},

year  = {2016},

date = {2016-01-01},

journal = {Intelligent Data Analysis},

volume = {20},

keywords = {CASI CAM CM, CIG UPM, Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Asheghan2016,

title = {Stability Analysis and Robust Control of Heart Beat Rate During Treadmill Exercise},

author = {Mohammad Mostafa Asheghan and Joaqu\'{i}n M\'{i}guez},

doi = {10.1016/j.automatica.2015.10.027},

issn = {00051098},

year  = {2016},

date = {2016-01-01},

journal = {Automatica},

volume = {63},

pages = {311--320},

abstract = {We investigate a nonlinear dynamical model of a human's heart beat rate (HBR) during a treadmill exercise. We begin with a rigorous analysis of the stability of the model that extends significantly the results available in the literature. In particular, we first identify a simple set of necessary and sufficient conditions for both input-state stability and Lyapunov stability of the system, and then prove that the same conditions also hold when the model parameters are subject to unknown but bounded perturbations. The second part of the paper is devoted to the design and analysis of a control structure for this model, where the treadmill speed plays the role of the control input and the output is the subject's HBR, which is intended to follow a prescribed pattern. We propose a simple control scheme, suitable for a practical implementation, and then analyze its performance. Specifically, we prove (i) that the same conditions that guarantee the stability of the system also ensure that the controller attains a desired level of performance (quantified in terms of the admissible deviation of the HBR from the prescribed profile) and (ii) that the controller is robust to bounded perturbations both in the system parameters and the control input. Numerical simulations are also presented in order to illustrate some of the theoretical results.},

keywords = {Cardiovascular system, Journal, Nonlinear systems, Robust control},

pubstate = {published},

tppubtype = {article}

}

@article{F.Pradier2016,

title = {Prior Design for Dependent Dirichlet Processes: An Application to Marathon Modeling},

author = {Melanie F. Pradier and Francisco J R Ruiz and Fernando Perez-Cruz},

editor = {Guy Brock},

url = {http://dx.plos.org/10.1371/journal.pone.0147402},

doi = {10.1371/journal.pone.0147402},

issn = {1932-6203},

year  = {2016},

date = {2016-01-01},

journal = {PLOS ONE},

volume = {11},

number = {1},

pages = {e0147402},

publisher = {Public Library of Science},

abstract = {This paper presents a novel application of Bayesian nonparametrics (BNP) for marathon data modeling. We make use of two well-known BNP priors, the single-p dependent Dirichlet process and the hierarchical Dirichlet process, in order to address two different problems. First, we study the impact of age, gender and environment on the runners' performance. We derive a fair grading method that allows direct comparison of runners regardless of their age and gender. Unlike current grading systems, our approach is based not only on top world records, but on the performances of all runners. The presented methodology for comparison of densities can be adopted in many other applications straightforwardly, providing an interesting perspective to build dependent Dirichlet processes. Second, we analyze the running patterns of the marathoners in time, obtaining information that can be valuable for training purposes. We also show that these running patterns can be used to predict finishing time given intermediate interval measurements. We apply our models to New York City, Boston and London marathons.},

keywords = {Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Guzman2015,

title = {Cooperative Optical Wireless Transmission for Improving Performance in Indoor Scenarios for Visible Light Communications},

author = {Borja Genoves Guzman and Alejandro Lancho Serrano and V\'{i}ctor Gil P Jimenez},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7389772},

doi = {10.1109/TCE.2015.7389772},

issn = {0098-3063},

year  = {2015},

date = {2015-11-01},

journal = {IEEE Transactions on Consumer Electronics},

volume = {61},

number = {4},

pages = {393--401},

publisher = {IEEE},

abstract = {In this paper, a novel cooperative transmission and reception scheme in Visible Light Communications (VLC) is proposed and evaluated. This new scheme provides improvements and reliability in large indoor scenarios, such as corridors, laboratories, shops or conference rooms, where the coverage needs to be obtained by using different access points when VLC is used. The main idea behind the proposal is a simple cooperative transmission scheme where the receiver terminal will obtain the signal from different access points at the same time. This proposal outperforms traditional VLC schemes, especially in Non-Line-of-Sight reception where around 3 dB of gain, with respect to traditional schemes, can be obtained for unoptimized parameters, and larger than 3 dB could easily be achieved. The cooperation is studied in terms of the percentage of light coming from the main access point and a parameter called sidelobes??? amplitude level. The performance is evaluated according to the location within the atto-cell.},

keywords = {CoMP, Cooperative transmission andreception, Interference, Journal, Nonlinear optics, Optical receivers, Proposals, Pulse Position Division Multiplexing, Radio frequency, VLC, Wireless communication},

pubstate = {published},

tppubtype = {article}

}

@article{Elvira2015bb,

title = {Efficient Multiple Importance Sampling Estimators},

author = {Victor Elvira and Luca Martino and David Luengo and Monica F Bugallo},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7105865},

doi = {10.1109/LSP.2015.2432078},

issn = {1070-9908},

year  = {2015},

date = {2015-10-01},

journal = {IEEE Signal Processing Letters},

volume = {22},

number = {10},

pages = {1757--1761},

publisher = {IEEE},

abstract = {Multiple importance sampling (MIS) methods use a set of proposal distributions from which samples are drawn. Each sample is then assigned an importance weight that can be obtained according to different strategies. This work is motivated by the trade-off between variance reduction and computational complexity of the different approaches (classical vs. deterministic mixture) available for the weight calculation. A new method that achieves an efficient compromise between both factors is introduced in this letter. It is based on forming a partition of the set of proposal distributions and computing the weights accordingly. Computer simulations show the excellent performance of the associated partial deterministic mixture MIS estimator.},

keywords = {Adaptive importance sampling, classical mixture approach, computational complexity, Computational efficiency, Computer Simulation, deterministic mixture, estimation theory, Journal, Monte Carlo methods, multiple importance sampling, multiple importance sampling estimator, partial deterministic mixture MIS estimator, Proposals, signal sampling, Sociology, Standards, variance reduction, weight calculation},

pubstate = {published},

tppubtype = {article}

}

@article{Mihaljevic2015,

title = {Classifying GABAergic interneurons with semi-supervised projected model-based clustering.},

author = {Bojan Mihaljevi\'{c} and Ruth Benavides-Piccione and Luis Guerra and Javier DeFelipe and Pedro Larra\~{n}aga and Concha Bielza},

url = {http://www.aiimjournal.com/article/S0933365714001481/fulltext http://cig.fi.upm.es/articles/2015/Mihaljevic-2015-AIIM.pdf},

doi = {10.1016/j.artmed.2014.12.010},

issn = {1873-2860},

year  = {2015},

date = {2015-09-01},

journal = {Artificial intelligence in medicine},

volume = {65},

number = {1},

pages = {49--59},

publisher = {Elsevier},

abstract = {OBJECTIVES: A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names. We sought to automatically classify digitally reconstructed interneuronal morphologies according to this scheme. Simultaneously, we sought to discover possible subtypes of these types that might emerge during automatic classification (clustering). We also investigated which morphometric properties were most relevant for this classification. MATERIALS AND METHODS: A set of 118 digitally reconstructed interneuronal morphologies classified into the common basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of the world's leading neuroscientists, quantified by five simple morphometric properties of the axon and four of the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. We then removed this class information for each type separately, and applied semi-supervised clustering to those cells (keeping the others' cluster membership fixed), to assess separation from other types and look for the formation of new groups (subtypes). We performed this same experiment unlabeling the cells of two types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixture of Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performed the described experiments on three different subsets of the data, formed according to how many experts agreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least 26 (47 neurons). RESULTS: Interneurons with more reliable type labels were classified more accurately. We classified HT cells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy, respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, and no subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette width and ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively, confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a single type also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometric properties were more relevant that dendritic ones, with the axonal polar histogram length in the [$pi$, 2$pi$) angle interval being particularly useful. CONCLUSIONS: The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heterogeneous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones for distinguishing among the CB, HT, LB, and MA interneuron types.},

keywords = {Automatic neuron classification, CASI CAM CM, Cerebral cortex, CIG UPM, Gaussian mixture models, Journal, Semi-supervised projected clustering},

pubstate = {published},

tppubtype = {article}

}

@article{Borchani2015,

title = {A survey on multi-output regression},

author = {Hanen Borchani and Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},

url = {http://doi.wiley.com/10.1002/widm.1157 http://cig.fi.upm.es/articles/2015/Borchani-2015-WDMKD.pdf},

doi = {10.1002/widm.1157},

issn = {19424787},

year  = {2015},

date = {2015-09-01},

journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},

volume = {5},

number = {5},

pages = {216--233},

abstract = {In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of multi-output regression. This study provides a survey on state-of-the-art multi-output regression methods, that are categorized as problem transformation and algorithm adaptation methods. In addition, we present the mostly used performance evaluation measures, publicly available data sets for multi-output regression real-world problems, as well as open-source software frameworks.},

keywords = {algorithm adaptation methods, CASI CAM CM, CIG UPM, Journal, Multi-output regression, multi-target regression, performance evaluation measure, problem transformation methods},

pubstate = {published},

tppubtype = {article}

}

@article{Martino2015bbb,

title = {An Adaptive Population Importance Sampler: Learning From Uncertainty},

author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7117437},

doi = {10.1109/TSP.2015.2440215},

issn = {1053-587X},

year  = {2015},

date = {2015-08-01},

journal = {IEEE Transactions on Signal Processing},

volume = {63},

number = {16},

pages = {4422--4437},

publisher = {IEEE},

abstract = {Monte Carlo (MC) methods are well-known computational techniques, widely used in different fields such as signal processing, communications and machine learning. An important class of MC methods is composed of importance sampling (IS) and its adaptive extensions, such as population Monte Carlo (PMC) and adaptive multiple IS (AMIS). In this paper, we introduce a novel adaptive and iterated importance sampler using a population of proposal densities. The proposed algorithm, named adaptive population importance sampling (APIS), provides a global estimation of the variables of interest iteratively, making use of all the samples previously generated. APIS combines a sophisticated scheme to build the IS estimators (based on the deterministic mixture approach) with a simple temporal adaptation (based on epochs). In this way, APIS is able to keep all the advantages of both AMIS and PMC, while minimizing their drawbacks. Furthermore, APIS is easily parallelizable. The cloud of proposals is adapted in such a way that local features of the target density can be better taken into account compared to single global adaptation procedures. The result is a fast, simple, robust, and high-performance algorithm applicable to a wide range of problems. Numerical results show the advantages of the proposed sampling scheme in four synthetic examples and a localization problem in a wireless sensor network.},

keywords = {Adaptive importance sampling, adaptive multiple IS, adaptive population importance sampler, AMIS, APIS, Estimation, Importance sampling, IS estimators, iterative estimation, iterative methods, Journal, MC methods, Monte Carlo (MC) methods, Monte Carlo methods, population Monte Carlo, Proposals, Signal processing algorithms, simple temporal adaptation, Sociology, Standards, Wireless sensor network, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {article}

}

@article{Moreno2015b,

title = {Bayesian Nonparametric Crowdsourcing},

author = {Pablo Garcia-Moreno and Yee Whye Teh and Fernando Perez-Cruz and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://www.jmlr.org/papers/volume16/moreno15a/moreno15a.pdf},

year  = {2015},

date = {2015-08-01},

journal = {Journal of Machine Learning Research},

volume = {16},

number = {August},

pages = {1607--1627},

abstract = {Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets. User annotations are often noisy, so methods to combine the annotations to produce reliable estimates of the ground truth are necessary. We claim that considering the existence of clusters of users in this combination step can improve the performance. This is especially important in early stages of crowdsourcing implementations, where the number of annotations is low. At this stage there is not enough information to accurately estimate the bias introduced by each annotator separately, so we have to resort to models that consider the statistical links among them. In addition, finding these clusters is interesting in itself as knowing the behavior of the pool of annotators allows implementing efficient active learning strategies. Based on this, we propose in this paper two new fully unsupervised models based on a Chinese Restaurant Process (CRP) prior and a hierarchical structure that allows inferring these groups jointly with the ground truth and the properties of the users. Efficient inference algorithms based on Gibbs sampling with auxiliary variables are proposed. Finally, we perform experiments, both on synthetic and real databases, to show the advantages of our models over state-of-the-art algorithms.},

keywords = {Bayesian nonparametrics, Dirichlet process, Gibbs sampling, Hierarchical clustering, Journal, Multiple annotators},

pubstate = {published},

tppubtype = {article}

}

@article{Read2015b,

title = {Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises},

author = {Jesse Read and Luca Martino and Pablo M Olmos and David Luengo},

url = {http://www.sciencedirect.com/science/article/pii/S0031320315000084},

year  = {2015},

date = {2015-06-01},

journal = {Pattern Recognition},

volume = {48},

number = {6},

pages = {2096--2106},

abstract = {Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier chains, in which the predictions of individual classifiers are cascaded along a chain, thus taking into account inter-label dependencies and improving the overall performance. Several varieties of classifier chain methods have been introduced, and many of them perform very competitively across a wide range of benchmark datasets. However, scalability limitations become apparent on larger datasets when modeling a fully cascaded chain. In particular, the methods' strategies for discovering and modeling a good chain structure constitutes a mayor computational bottleneck. In this paper, we present the classifier trellis (CT) method for scalable multi-label classification. We compare CT with several recently proposed classifier chain methods to show that it occupies an important niche: it is highly competitive on standard multi-label problems, yet it can also scale up to thousands or even tens of thousands of labels.},

keywords = {Bayesian networks, classifer chains, Journal, Multi-label classification, multi-output prediction, structured inference},

pubstate = {published},

tppubtype = {article}

}

@article{Varando2015a,

title = {Decision functions for chain classifiers based on Bayesian networks for multi-label classification},

author = {Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},

url = {http://www.researchgate.net/publication/279069321_Decision_functions_for_chain_classifiers_based_on_Bayesian_networks_for_multi-label_classification http://cig.fi.upm.es/node/887},

doi = {10.1016/j.ijar.2015.06.006},

issn = {0888613X},

year  = {2015},

date = {2015-06-01},

journal = {International Journal of Approximate Reasoning},

abstract = {Multi-label classification problems require each instance to be assigned a subset of a defined set of labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of binary classes. In this paper we study the decision boundaries of two widely used approaches for building multi-label classifiers, when Bayesian network-augmented naive Bayes classifiers are used as base models: Binary relevance method and chain classifiers. In particular extending previous single-label results to multi-label chain classifiers, we find polynomial expressions for the multi-valued decision functions associated with these methods. We prove upper boundings on the expressive power of both methods and we prove that chain classifiers provide a more expressive model than the binary relevance method Decision functions for chain classifiers based on Bayesian networks for multi-label classification. Available from: http://www.researchgate.net/publication/279069321_Decision_functions_for_chain_classifiers_based_on_Bayesian_networks_for_multi-label_classification [accessed Nov 15, 2015].},

keywords = {CASI CAM CM, CIG UPM, Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Olmos2015bb,

title = {A Scaling Law to Predict the Finite-Length Performance of Spatially-Coupled LDPC Codes},

author = {Pablo M Olmos and Rudiger Urbanke},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7086074},

doi = {10.1109/TIT.2015.2422816},

issn = {0018-9448},

year  = {2015},

date = {2015-06-01},

journal = {IEEE Transactions on Information Theory},

volume = {61},

number = {6},

pages = {3164--3184},

abstract = {Spatially-coupled low-density parity-check (SC-LDPC) codes are known to have excellent asymptotic properties. Much less is known regarding their finite-length performance. We propose a scaling law to predict the error probability of finite-length spatially coupled code ensembles when transmission takes place over the binary erasure channel. We discuss how the parameters of the scaling law are connected to fundamental quantities appearing in the asymptotic analysis of these ensembles and we verify that the predictions of the scaling law fit well to the data derived from simulations over a wide range of parameters. The ultimate goal of this line of research is to develop analytic tools for the design of SC-LDPC codes under practical constraints.},

keywords = {asymptotic analysis, asymptotic properties, binary erasure channel, Channel Coding, Codes on graphs, Couplings, Decoding, Differential equations, error probability, finite length performance, finite length spatially coupled code, finite-length code performance, finite-length performance, Iterative decoding, iterative decoding thresholds, Journal, parity check codes, Probability, SC-LDPC codes, scaling law, Sockets, spatially coupled LDPC codes, spatially-coupled LDPC codes},

pubstate = {published},

tppubtype = {article}

}

@article{Ruiz2015b,

title = {A Generative Model for Predicting Outcomes in College Basketball},

author = {Francisco J R Ruiz and Fernando Perez-Cruz},

url = {http://www.degruyter.com/view/j/jqas.2015.11.issue-1/jqas-2014-0055/jqas-2014-0055.xml},

doi = {10.1515/jqas-2014-0055},

issn = {1559-0410},

year  = {2015},

date = {2015-03-01},

journal = {Journal of Quantitative Analysis in Sports},

volume = {11},

number = {1 Special Issue},

pages = {39--52},

abstract = {We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.},

keywords = {CASI CAM CM, GAMMA-L+ UC3M, Journal, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference},

pubstate = {published},

tppubtype = {article}

}

@article{Koblents2014b,

title = {A Population Monte Carlo Scheme with Transformed Weights and Its Application to Stochastic Kinetic Models},

author = {Eugenia Koblents and Joaquin Miguez},

url = {http://link.springer.com/10.1007/s11222-013-9440-2 http://gts.tsc.uc3m.es/wp-content/uploads/2014/01/NPMC_A-population-Monte-Carlo-scheme-with-transformed_jma.pdf},

doi = {10.1007/s11222-013-9440-2},

issn = {0960-3174},

year  = {2015},

date = {2015-03-01},

journal = {Statistics and Computing},

volume = {25},

number = {2},

pages = {407--425},

abstract = {This paper addresses the Monte Carlo approximation of posterior probability distributions. In particular, we consider the population Monte Carlo (PMC) technique, which is based on an iterative importance sampling (IS) approach. An important drawback of this methodology is the degeneracy of the importance weights (IWs) when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a new method that performs a nonlinear transformation of the IWs. This operation reduces the weight variation, hence it avoids degeneracy and increases the efficiency of the IS scheme, specially when drawing from proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a simple problem that enables us to discuss the main features of the proposed technique. As a practical application, we have also considered the challenging problem of estimating the rate parameters of a stochastic kinetic model (SKM). SKMs are multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.},

keywords = {COMPREHENSION, degeneracy of importance weights, Importance sampling, Journal, population Monte Carlo, Stochastic kinetic models},

pubstate = {published},

tppubtype = {article}

}

@article{Varando2015b,

title = {Conditional Density Approximations with Mixtures of Polynomials},

author = {Gherardo Varando and Pedro L L\'{o}pez-Cruz and Thomas D Nielsen and Pedro Larra\~{n}aga and Concha Bielza},

url = {http://doi.wiley.com/10.1002/int.21699 http://cig.fi.upm.es/articles/2015/Varando-2015-IJIS.pdf},

doi = {10.1002/int.21699},

issn = {08848173},

year  = {2015},

date = {2015-03-01},

journal = {International Journal of Intelligent Systems},

volume = {30},

number = {3},

pages = {236--264},

abstract = {Mixtures of polynomials (MoPs) are a nonparametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multidimensional (marginal) MoPs from data have recently been proposed. In this paper, we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.},

keywords = {CASI CAM CM, CIG UPM, Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Varando2015c,

title = {Decision boundary for discrete Bayesian network classifiers},

author = {Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},

url = {http://cig.fi.upm.es/node/881 http://cig.fi.upm.es/articles/2015/Varando-2015-JMLR.pdf},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Machine Learning Research},

abstract = {Bayesian network classi ers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classi ers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V -structures in the predictor sub-graph, we are also able to prove that this family of polynomials does indeed characterize the speci c classi er considered. We then use this representation to bound the number of decision functions representable by Bayesian network classi ers with a given structure},

keywords = {Bayesian networks, CASI CAM CM, CIG UPM, decision boundary, Journal, Lagrange basis, polynomial, supervised classication, threshold function},

pubstate = {published},

tppubtype = {article}

}

@article{Manzano2015b,

title = {Cognitive Self-Scheduled Mechanism for Access Control in Noisy Vehicular Ad Hoc Networks},

author = {Mario Manzano and Felipe Espinosa and \'{A}ngel M Bravo-Santos and Alfredo Gardel-Vicente},

url = {http://www.hindawi.com/journals/mpe/2015/354292/ http://dx.doi.org/10.1155/2015/354292},

doi = {10.1155/2015/354292},

year  = {2015},

date = {2015-01-01},

journal = {Mathematical Problems in Engineering.},

volume = {2015},

pages = {1--12},

abstract = {Within the challenging environment of intelligent transportation systems (ITS), networked control systems such as platooning guidance of autonomous vehicles require innovative mechanisms to provide real-time communications. Although several proposals are currently under discussion, the design of a rapid, efficient, flexible, and reliable medium access control mechanism which meets the specific constraints of such real-time communications applications remains unsolved in this highly dynamic environment. However, cognitive radio (CR) combines the capacity to sense the radio spectrum with the flexibility to adapt to transmission parameters in order to maximize system performance and has thus become an effective approach for the design of dynamic spectrum access (DSA) mechanisms. This paper presents the enhanced noncooperative cognitive division multiple access (ENCCMA) proposal combining time division multiple access (TDMA) and frequency division multiple access (FDMA) schemes with CR techniques to obtain a mechanism fulfilling the requirements of real-time communications. The analysis presented here considers the IEEE WAVE and 802.11p as reference standards; however, the proposed medium access control (MAC) mechanism can be adapted to operate on the physical layer of different standards. The mechanism also offers the advantage of avoiding signaling, thus enhancing system autonomy as well as behavior in adverse scenarios.},

keywords = {Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Bravo-Santos2014bb,

title = {Detectors for Cooperative Mesh Networks with Decode-and-Forward Relays},

author = {\'{A}ngel M Bravo-Santos and Petar M Djuric},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6928514},

doi = {10.1109/TSP.2014.2364016},

issn = {1053-587X},

year  = {2015},

date = {2015-01-01},

journal = {IEEE Transactions on Signal Processing},

volume = {63},

number = {1},

pages = {5--17},

publisher = {IEEE},

abstract = {We consider mesh networks composed of groups of relaying nodes which operate in decode-andforward mode. Each node from a group relays information to all the nodes in the next group. We study these networks in two setups, one where the nodes have complete state information about the channels through which they receive the signals, and another when they only have the statistics of the channels. We derive recursive expressions for the probabilities of errors of the nodes and present several implementations of detectors used in these networks. We compare the mesh networks with multi-hop networks formed by a set of parallel sections of multiple relaying nodes. We demonstrate with numerous simulations that there are significant improvements in performance of mesh over multi-hop networks in various scenarios.},

keywords = {Cooperative systems, Detectors, Journal, Mesh networks, Modulation, Relays, spread spectrum communication, Wireless communication},

pubstate = {published},

tppubtype = {article}

}