Decayed MCMC Filtering Export

@inproceedings{citeulike:5778811, abstract = {Filtering---estimating the state of a partially observable Markov process from a sequence of observations---is one of the most widely studied problems in control theory, AI, and computational statistics. Exact computation of the posterior distribution is generally intractable for large discrete systems and for nonlinear continuous systems, so a good deal of effort has gone into developing robust approximation algorithms.}, author = {Marthi, Bhaskara and Pasula, Hanna and Russell, Stuart and Peres, Yuval}, booktitle = {In Proc. 18th UAI}, citeulike-article-id = {5778811}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.3258}, keywords = {2002, mcmc, online\_learning, tracking, uai}, pages = {319--326}, posted-at = {2009-09-13 04:11:34}, priority = {2}, title = {Decayed MCMC Filtering}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.3258}, volume = {21}, year = {2002} }

TY - CONF ID - citeulike:5778811 L3 - citeulike-article-id:5778811 N2 - Filtering---estimating the state of a partially observable Markov process from a sequence of observations---is one of the most widely studied problems in control theory, AI, and computational statistics. Exact computation of the posterior distribution is generally intractable for large discrete systems and for nonlinear continuous systems, so a good deal of effort has gone into developing robust approximation algorithms. EP - 326 TI - Decayed MCMC Filtering VL - 21 T2 - In Proc. 18th UAI SP - 319 KW - 2002 KW - mcmc KW - online_learning KW - tracking KW - uai AU - Marthi, Bhaskara AU - Pasula, Hanna AU - Russell, Stuart AU - Peres, Yuval PY - 2002/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.3258 ER -