### 2019

Miguez, Joaquín; Lacasa, Lucas; Martínez-Ordóñez, José A.; Mariño, Inés P.

Multilayer Models of Random Sequences: Representability and Inference via Nonlinear Population Monte Carlo Inproceedings

In: 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019.

Links | BibTeX | Tags: Markov chains, Multilayer networks, population Monte Carlo, random sequences

@inproceedings{JMiguez19c,

title = {Multilayer Models of Random Sequences: Representability and Inference via Nonlinear Population Monte Carlo},

author = {Joaquín Miguez and Lucas Lacasa and José A. Martínez-Ordóñez and Inés P. Mariño},

doi = {10.1109/CAMSAP45676.2019.9022529},

year = {2019},

date = {2019-12-15},

booktitle = {2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},

keywords = {Markov chains, Multilayer networks, population Monte Carlo, random sequences},

pubstate = {published},

tppubtype = {inproceedings}

}

### 2014

Martino, Luca; Elvira, Víctor; Luengo, David; Artés-Rodríguez, Antonio; Corander, Jukka

Orthogonal MCMC Algorithms Inproceedings

In: 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, 2014.

Abstract | Links | BibTeX | Tags: Bayesian inference, Markov Chain Monte Carlo (MCMC), Parallel Chains, population Monte Carlo

@inproceedings{Martino2014b,

title = {Orthogonal MCMC Algorithms},

author = {Luca Martino and Víctor Elvira and David Luengo and Antonio Artés-Rodríguez and Jukka Corander},

url = {http://edas.info/p15153#S1569490857},

year = {2014},

date = {2014-01-01},

booktitle = {2014 IEEE Workshop on Statistical Signal Processing (SSP 2014)},

address = {Gold Coast},

abstract = {Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A wellknown 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 using another MCMC technique working on the entire population of current states. These parallel “vertical” chains are led by random-walk proposals, whereas the “horizontal” MCMC uses a independent proposal, which can be easily adapted by making use of all the generated samples. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error, as well as robustness w.r.t. to initial values and parameter choice.},

keywords = {Bayesian inference, Markov Chain Monte Carlo (MCMC), Parallel Chains, population Monte Carlo},

pubstate = {published},

tppubtype = {inproceedings}

}

### 2013

Koblents, Eugenia; Miguez, Joaquin

A Population Monte Carlo Scheme for Computational Inference in High Dimensional Spaces Inproceedings

In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6318–6322, IEEE, Vancouver, 2013, ISSN: 1520-6149.

Abstract | Links | BibTeX | Tags: Approximation methods, computational inference, degeneracy of importance weights, high dimensional spaces, Importance sampling, importance weights, iterative importance sampling, iterative methods, mixture-PMC, mixture-PMC algorithm, Monte Carlo methods, MPMC, nonlinear transformations, population Monte Carlo, population Monte Carlo scheme, Probability density function, probability distributions, Proposals, Sociology, Standards

@inproceedings{Koblents2013a,

title = {A Population Monte Carlo Scheme for Computational Inference in High Dimensional Spaces},

author = {Eugenia Koblents and Joaquin Miguez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6638881},

issn = {1520-6149},

year = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},

pages = {6318--6322},

publisher = {IEEE},

address = {Vancouver},

abstract = {In this paper we address the Monte Carlo approximation of integrals with respect to probability distributions in high-dimensional spaces. In particular, we investigate the population Monte Carlo (PMC) scheme, which is based on an iterative importance sampling (IS) approach. Both IS and PMC suffer from the well known problem of degeneracy of the importance weights (IWs), which is closely related to the curse-of-dimensionality, and limits their applicability in large-scale practical problems. In this paper we investigate a novel PMC scheme that consists in performing nonlinear transformations of the IWs in order to smooth their variations and avoid degeneracy. We apply the modified IS scheme to the well-known mixture-PMC (MPMC) algorithm, which constructs the importance functions as mixtures of kernels. We present numerical results that show how the modified version of MPMC clearly outperforms the original scheme.},

keywords = {Approximation methods, computational inference, degeneracy of importance weights, high dimensional spaces, Importance sampling, importance weights, iterative importance sampling, iterative methods, mixture-PMC, mixture-PMC algorithm, Monte Carlo methods, MPMC, nonlinear transformations, population Monte Carlo, population Monte Carlo scheme, Probability density function, probability distributions, Proposals, Sociology, Standards},

pubstate = {published},

tppubtype = {inproceedings}

}