Orthogonal MCMC algorithms

Luca Martino, Victor Elvira, David Luengo, Antonio Artes-Rodriguez, Jukka Corander
2014 2014 IEEE Workshop on Statistical Signal Processing (SSP)  
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
more » ... roposal, 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.
doi:10.1109/ssp.2014.6884651 dblp:conf/ssp/MartinoELAC14 fatcat:gfliik5lhreuzf4rgi5okjqd4i