{"id":11705,"date":"2014-03-21T23:33:49","date_gmt":"2014-03-21T21:33:49","guid":{"rendered":"http:\/\/hgpu.org\/?p=11705"},"modified":"2015-08-27T01:16:23","modified_gmt":"2015-08-26T22:16:23","slug":"concurrent-learning-of-a-probabilistic-graphical-model-on-the-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11705","title":{"rendered":"Concurrent learning of a Probabilistic Graphical Model on the GPU"},"content":{"rendered":"<p>We introduce an algorithm for determining optimal transition paths between given configurations. The solution is obtained by solving variational equations for Freidlin\u2013Wentzell action functionals. One of the applications of the method presented is a system controlling motion and redeployment between unit\u2019s formations. The efficiency of the algorithm has been evaluated in a simple sandbox environment implemented with the use of the NVIDIA CUDA technology.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We introduce an algorithm for determining optimal transition paths between given configurations. The solution is obtained by solving variational equations for Freidlin\u2013Wentzell action functionals. One of the applications of the method presented is a system controlling motion and redeployment between unit\u2019s formations. The efficiency of the algorithm has been evaluated in a simple sandbox environment [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,3],"tags":[957,14,20,379,442],"class_list":["post-11705","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","tag-bayesian","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-path-problems"],"views":2626,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11705","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=11705"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11705\/revisions"}],"predecessor-version":[{"id":14482,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11705\/revisions\/14482"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11705"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11705"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11705"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}