### 2015

Elvira, Victor; Martino, Luca; Luengo, David; Corander, Jukka

A Gradient Adaptive Population Importance Sampler Inproceedings

In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4075–4079, IEEE, Brisbane, 2015, ISBN: 978-1-4673-6997-8.

Abstract | Links | BibTeX | Tags: adaptive extensions, adaptive importance sampler, Adaptive importance sampling, gradient adaptive population, gradient matrix, Hamiltonian Monte Carlo, Hessian matrices, Hessian matrix, learning (artificial intelligence), Machine learning, MC methods, Monte Carlo, Monte Carlo methods, population Monte Carlo (PMC), proposal densities, Signal processing, Sociology, statistics, target distribution

@inproceedings{Elvira2015a,

title = {A Gradient Adaptive Population Importance Sampler},

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

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178737 http://www.tsc.uc3m.es/~velvira/papers/ICASSP2015_elvira.pdf},

doi = {10.1109/ICASSP.2015.7178737},

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 = {4075--4079},

publisher = {IEEE},

address = {Brisbane},

abstract = {Monte Carlo (MC) methods are widely used in signal processing and machine learning. A well-known class of MC methods is composed of importance sampling and its adaptive extensions (e.g., population Monte Carlo). In this paper, we introduce an adaptive importance sampler using a population of proposal densities. The novel algorithm dynamically optimizes the cloud of proposals, adapting them using information about the gradient and Hessian matrix of the target distribution. Moreover, a new kind of interaction in the adaptation of the proposal densities is introduced, establishing a trade-off between attaining a good performance in terms of mean square error and robustness to initialization.},

keywords = {adaptive extensions, adaptive importance sampler, Adaptive importance sampling, gradient adaptive population, gradient matrix, Hamiltonian Monte Carlo, Hessian matrices, Hessian matrix, learning (artificial intelligence), Machine learning, MC methods, Monte Carlo, Monte Carlo methods, population Monte Carlo (PMC), proposal densities, Signal processing, Sociology, statistics, target distribution},

pubstate = {published},

tppubtype = {inproceedings}

}

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

Smelly Parallel MCMC Chains Inproceedings

In: 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 = {Luca Martino and Victor Elvira and David Luengo and Antonio Artés-Rodríguez and Jukka Corander},

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}

}