{"action":"created","issue":{"url":"https://api.github.com/repos/BIDData/BIDMach/issues/46","labels_url":"https://api.github.com/repos/BIDData/BIDMach/issues/46/labels{/name}","comments_url":"https://api.github.com/repos/BIDData/BIDMach/issues/46/comments","events_url":"https://api.github.com/repos/BIDData/BIDMach/issues/46/events","html_url":"https://github.com/BIDData/BIDMach/issues/46","id":72188628,"number":46,"title":"parallel graph processing with BIDmach?","user":{"login":"Navgeet","id":1017820,"avatar_url":"https://avatars.githubusercontent.com/u/1017820?v=3","gravatar_id":"","url":"https://api.github.com/users/Navgeet","html_url":"https://github.com/Navgeet","followers_url":"https://api.github.com/users/Navgeet/followers","following_url":"https://api.github.com/users/Navgeet/following{/other_user}","gists_url":"https://api.github.com/users/Navgeet/gists{/gist_id}","starred_url":"https://api.github.com/users/Navgeet/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Navgeet/subscriptions","organizations_url":"https://api.github.com/users/Navgeet/orgs","repos_url":"https://api.github.com/users/Navgeet/repos","events_url":"https://api.github.com/users/Navgeet/events{/privacy}","received_events_url":"https://api.github.com/users/Navgeet/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":1,"created_at":"2015-04-30T15:06:06Z","updated_at":"2015-09-28T04:28:57Z","closed_at":"2015-09-28T04:28:57Z","body":"Hi. Is it possible to do parallel graph processing using BIDMach? Specifically I'm looking to implement belief propagation on factor graphs."},"comment":{"url":"https://api.github.com/repos/BIDData/BIDMach/issues/comments/143639203","html_url":"https://github.com/BIDData/BIDMach/issues/46#issuecomment-143639203","issue_url":"https://api.github.com/repos/BIDData/BIDMach/issues/46","id":143639203,"user":{"login":"jcanny","id":1195924,"avatar_url":"https://avatars.githubusercontent.com/u/1195924?v=3","gravatar_id":"","url":"https://api.github.com/users/jcanny","html_url":"https://github.com/jcanny","followers_url":"https://api.github.com/users/jcanny/followers","following_url":"https://api.github.com/users/jcanny/following{/other_user}","gists_url":"https://api.github.com/users/jcanny/gists{/gist_id}","starred_url":"https://api.github.com/users/jcanny/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jcanny/subscriptions","organizations_url":"https://api.github.com/users/jcanny/orgs","repos_url":"https://api.github.com/users/jcanny/repos","events_url":"https://api.github.com/users/jcanny/events{/privacy}","received_events_url":"https://api.github.com/users/jcanny/received_events","type":"User","site_admin":false},"created_at":"2015-09-28T04:28:57Z","updated_at":"2015-09-28T04:28:57Z","body":"You might want to take a look at the BayesNet Learner that we've been working on recently. Its doing inference on discrete graphical models, but is quite easily extensible to factor graphs. In fact we do normalization on the fly which means its already set up for arbitrary (un-normalized) factors. "}}