## 2015 |

## Journal Articles |

Elvira, Victor ; Martino, Luca ; Luengo, David ; Bugallo, Monica F Efficient Multiple Importance Sampling Estimators Journal Article IEEE Signal Processing Letters, 22 (10), pp. 1757–1761, 2015, ISSN: 1070-9908. Abstract | Links | BibTeX | Tags: 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 @article{Elvira2015b, title = {Efficient Multiple Importance Sampling Estimators}, author = {Elvira, Victor and Martino, Luca and Luengo, David and Bugallo, Monica F.}, 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} } 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. |

## Inproceedings |

Martino, Luca ; Elvira, Victor ; Luengo, David ; Artés-Rodríguez, Antonio ; Corander, J Smelly Parallel MCMC Chains Inproceedings 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4070–4074, IEEE, Brisbane, 2015, ISBN: 978-1-4673-6997-8. Abstract | Links | BibTeX | Tags: Bayesian inference, learning (artificial intelligence), Machine learning, Markov chain Monte Carlo, Markov chain Monte Carlo algorithms, Markov processes, MC methods, MCMC algorithms, MCMC scheme, mean square error, mean square error methods, Monte Carlo methods, optimisation, parallel and interacting chains, Probability density function, Proposals, robustness, Sampling methods, Signal processing, Signal processing algorithms, signal sampling, smelly parallel chains, smelly parallel MCMC chains, Stochastic optimization @inproceedings{Martino2015a, title = {Smelly Parallel MCMC Chains}, author = {Martino, Luca and Elvira, Victor and Luengo, David and Artés-Rodríguez, Antonio and Corander, J.}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178736 http://www.tsc.uc3m.es/~velvira/papers/ICASSP2015_martino.pdf}, doi = {10.1109/ICASSP.2015.7178736}, isbn = {978-1-4673-6997-8}, year = {2015}, date = {2015-04-01}, booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages = {4070--4074}, publisher = {IEEE}, address = {Brisbane}, abstract = {Monte Carlo (MC) methods are useful tools for Bayesian inference and stochastic optimization that have been widely applied in signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information, thus yielding a faster exploration of the state space. The interaction is carried out generating a dynamic repulsion among the “smelly” parallel chains that takes into account the entire population of current states. The ergodicity of the scheme and its relationship with other sampling methods are discussed. Numerical results show the advantages of the proposed approach in terms of mean square error, robustness w.r.t. to initial values and parameter choice.}, keywords = {Bayesian inference, learning (artificial intelligence), Machine learning, Markov chain Monte Carlo, Markov chain Monte Carlo algorithms, Markov processes, MC methods, MCMC algorithms, MCMC scheme, mean square error, mean square error methods, Monte Carlo methods, optimisation, parallel and interacting chains, Probability density function, Proposals, robustness, Sampling methods, Signal processing, Signal processing algorithms, signal sampling, smelly parallel chains, smelly parallel MCMC chains, Stochastic optimization}, pubstate = {published}, tppubtype = {inproceedings} } Monte Carlo (MC) methods are useful tools for Bayesian inference and stochastic optimization that have been widely applied in signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information, thus yielding a faster exploration of the state space. The interaction is carried out generating a dynamic repulsion among the “smelly” parallel chains that takes into account the entire population of current states. The ergodicity of the scheme and its relationship with other sampling methods are discussed. Numerical results show the advantages of the proposed approach in terms of mean square error, robustness w.r.t. to initial values and parameter choice. |

## 2010 |

## Inproceedings |

Koch, Tobias ; Lapidoth, Amos Increased Capacity per Unit-Cost by Oversampling Inproceedings 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel, pp. 000684–000688, IEEE, Eliat, 2010, ISBN: 978-1-4244-8681-6. Abstract | Links | BibTeX | Tags: AWGN, AWGN channels, bandlimited Gaussian channel, channel capacity, Gaussian channels, increased capacity per unit cost, Information rates, one bit output quantizer, oversampling, quantisation (signal), quantization, sampling rate recovery, signal sampling @inproceedings{Koch2010, title = {Increased Capacity per Unit-Cost by Oversampling}, author = {Koch, Tobias and Lapidoth, Amos}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5662127}, isbn = {978-1-4244-8681-6}, year = {2010}, date = {2010-01-01}, booktitle = {2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel}, pages = {000684--000688}, publisher = {IEEE}, address = {Eliat}, abstract = {It is demonstrated that doubling the sampling rate recovers some of the loss in capacity incurred on the bandlimited Gaussian channel with a one-bit output quantizer.}, keywords = {AWGN, AWGN channels, bandlimited Gaussian channel, channel capacity, Gaussian channels, increased capacity per unit cost, Information rates, one bit output quantizer, oversampling, quantisation (signal), quantization, sampling rate recovery, signal sampling}, pubstate = {published}, tppubtype = {inproceedings} } It is demonstrated that doubling the sampling rate recovers some of the loss in capacity incurred on the bandlimited Gaussian channel with a one-bit output quantizer. |

## 2009 |

## Inproceedings |

Martino, Luca ; Miguez, Joaquin A Novel Rejection Sampling Scheme for Posterior Probability Distributions Inproceedings 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2921–2924, IEEE, Taipei, 2009, ISSN: 1520-6149. Abstract | Links | BibTeX | Tags: Additive noise, arbitrary target probability distributions, Bayes methods, Bayesian methods, Monte Carlo integration, Monte Carlo methods, Monte Carlo techniques, Overbounding, posterior probability distributions, Probability density function, Probability distribution, Proposals, Rejection sampling, rejection sampling scheme, Sampling methods, Signal processing algorithms, signal sampling, Upper bound @inproceedings{Martino2009, title = {A Novel Rejection Sampling Scheme for Posterior Probability Distributions}, author = {Martino, Luca and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4960235}, issn = {1520-6149}, year = {2009}, date = {2009-01-01}, booktitle = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing}, pages = {2921--2924}, publisher = {IEEE}, address = {Taipei}, abstract = {Rejection sampling (RS) is a well-known method to draw from arbitrary target probability distributions, which has important applications by itself or as a building block for more sophisticated Monte Carlo techniques. The main limitation to the use of RS is the need to find an adequate upper bound for the ratio of the target probability density function (pdf) over the proposal pdf from which the samples are generated. There are no general methods to analytically find this bound, except in the particular case in which the target pdf is log-concave. In this paper we adopt a Bayesian view of the problem and propose a general RS scheme to draw from the posterior pdf of a signal of interest using its prior density as a proposal function. The method enables the analytical calculation of the bound and can be applied to a large class of target densities. We illustrate its use with a simple numerical example.}, keywords = {Additive noise, arbitrary target probability distributions, Bayes methods, Bayesian methods, Monte Carlo integration, Monte Carlo methods, Monte Carlo techniques, Overbounding, posterior probability distributions, Probability density function, Probability distribution, Proposals, Rejection sampling, rejection sampling scheme, Sampling methods, Signal processing algorithms, signal sampling, Upper bound}, pubstate = {published}, tppubtype = {inproceedings} } Rejection sampling (RS) is a well-known method to draw from arbitrary target probability distributions, which has important applications by itself or as a building block for more sophisticated Monte Carlo techniques. The main limitation to the use of RS is the need to find an adequate upper bound for the ratio of the target probability density function (pdf) over the proposal pdf from which the samples are generated. There are no general methods to analytically find this bound, except in the particular case in which the target pdf is log-concave. In this paper we adopt a Bayesian view of the problem and propose a general RS scheme to draw from the posterior pdf of a signal of interest using its prior density as a proposal function. The method enables the analytical calculation of the bound and can be applied to a large class of target densities. We illustrate its use with a simple numerical example. |

Martino, Luca ; Miguez, Joaquin An Adaptive Accept/Reject Sampling Algorithm for Posterior Probability Distributions Inproceedings 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 45–48, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3. Abstract | Links | BibTeX | Tags: adaptive accept/reject sampling, Adaptive rejection sampling, arbitrary target probability distributions, Computer Simulation, Filtering, Monte Carlo integration, Monte Carlo methods, posterior probability distributions, Probability, Probability density function, Probability distribution, Proposals, Rejection sampling, Sampling methods, sensor networks, Signal processing algorithms, signal sampling, Testing @inproceedings{Martino2009b, title = {An Adaptive Accept/Reject Sampling Algorithm for Posterior Probability Distributions}, author = {Martino, Luca and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278644}, isbn = {978-1-4244-2709-3}, year = {2009}, date = {2009-01-01}, booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing}, pages = {45--48}, publisher = {IEEE}, address = {Cardiff}, abstract = {Accept/reject sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. In this paper we introduce an adaptive method to build a sequence of proposal pdf's that approximate the target density and hence can ensure a high acceptance rate. In order to illustrate the application of the method we design an accept/reject particle filter and then assess its performance and sampling efficiency numerically, by means of computer simulations.}, keywords = {adaptive accept/reject sampling, Adaptive rejection sampling, arbitrary target probability distributions, Computer Simulation, Filtering, Monte Carlo integration, Monte Carlo methods, posterior probability distributions, Probability, Probability density function, Probability distribution, Proposals, Rejection sampling, Sampling methods, sensor networks, Signal processing algorithms, signal sampling, Testing}, pubstate = {published}, tppubtype = {inproceedings} } Accept/reject sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. In this paper we introduce an adaptive method to build a sequence of proposal pdf's that approximate the target density and hence can ensure a high acceptance rate. In order to illustrate the application of the method we design an accept/reject particle filter and then assess its performance and sampling efficiency numerically, by means of computer simulations. |