A sequential particle filter method for static models

@misc{citeulike:2447705, abstract = {Particle filter methods are complex inference procedures, which combine importance sampling and Monte Carlo schemes, in order to consistently explore a sequence of multiple distributions of interest. The purpose of this article is to show that such methods can also offer an efficient estimation tool in \"static\" setups; in this case, \π(\θ|y\_1, ..., y\_N) is the only posterior distribution of interest but the preliminary exploration of partial posteriors \π(\θ|y\_1, ..., y\_N) (n \< N) ...}, author = {Chopin, N. }, citeulike-article-id = {2447705}, keywords = {bayesian, filter, particle}, posted-at = {2008-02-29 13:44:19}, priority = {2}, title = {A sequential particle filter method for static models}, url = {http://citeseer.ist.psu.edu/385654.html}, year = {2002} }

TY - GEN ID - citeulike:2447705 L3 - citeulike-article-id:2447705 TI - A sequential particle filter method for static models N2 - Particle filter methods are complex inference procedures, which combine importance sampling and Monte Carlo schemes, in order to consistently explore a sequence of multiple distributions of interest. The purpose of this article is to show that such methods can also offer an efficient estimation tool in "static" setups; in this case, π(θ|y_1, ..., y_N) is the only posterior distribution of interest but the preliminary exploration of partial posteriors π(θ|y_1, ..., y_N) (n < N) ... KW - bayesian KW - filter KW - particle AU - Chopin, N PY - 2002/// UR - http://citeseer.ist.psu.edu/385654.html ER -