Name
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. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP) | Markov Chain Monte Carlo, parallel and interacting chains, Bayesian inference |
Field | DocType | ISSN |
Monte Carlo method,Mathematical optimization,Markov chain Monte Carlo,Coupling from the past,Computer science,Particle filter,Markov chain,Hybrid Monte Carlo,Parallel tempering,Monte Carlo integration | Conference | 1520-6149 |
Citations | PageRank | References |
3 | 0.40 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Martino | 1 | 309 | 34.86 |
| Victor Elvira | 2 | 20 | 2.50 |
| David Luengo | 3 | 230 | 25.55 |
| Antonio Artés-Rodríguez | 4 | 206 | 34.76 |
| Jukka Corander | 5 | 17 | 3.73 |