{"action":"created","issue":{"url":"https://api.github.com/repos/tensorprob/tensorprob/issues/59","repository_url":"https://api.github.com/repos/tensorprob/tensorprob","labels_url":"https://api.github.com/repos/tensorprob/tensorprob/issues/59/labels{/name}","comments_url":"https://api.github.com/repos/tensorprob/tensorprob/issues/59/comments","events_url":"https://api.github.com/repos/tensorprob/tensorprob/issues/59/events","html_url":"https://github.com/tensorprob/tensorprob/issues/59","id":138002448,"number":59,"title":"Simultaneous PDF","user":{"login":"cranmer","id":4458890,"avatar_url":"https://avatars.githubusercontent.com/u/4458890?v=3","gravatar_id":"","url":"https://api.github.com/users/cranmer","html_url":"https://github.com/cranmer","followers_url":"https://api.github.com/users/cranmer/followers","following_url":"https://api.github.com/users/cranmer/following{/other_user}","gists_url":"https://api.github.com/users/cranmer/gists{/gist_id}","starred_url":"https://api.github.com/users/cranmer/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/cranmer/subscriptions","organizations_url":"https://api.github.com/users/cranmer/orgs","repos_url":"https://api.github.com/users/cranmer/repos","events_url":"https://api.github.com/users/cranmer/events{/privacy}","received_events_url":"https://api.github.com/users/cranmer/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":3,"created_at":"2016-03-02T21:48:10Z","updated_at":"2016-03-02T23:38:51Z","closed_at":null,"body":"Do you have ability to combine p(x|theta) and p(y|theta) when you have N events for x and M events for y. As in RooSimultaneous?"},"comment":{"url":"https://api.github.com/repos/tensorprob/tensorprob/issues/comments/191494522","html_url":"https://github.com/tensorprob/tensorprob/issues/59#issuecomment-191494522","issue_url":"https://api.github.com/repos/tensorprob/tensorprob/issues/59","id":191494522,"user":{"login":"cranmer","id":4458890,"avatar_url":"https://avatars.githubusercontent.com/u/4458890?v=3","gravatar_id":"","url":"https://api.github.com/users/cranmer","html_url":"https://github.com/cranmer","followers_url":"https://api.github.com/users/cranmer/followers","following_url":"https://api.github.com/users/cranmer/following{/other_user}","gists_url":"https://api.github.com/users/cranmer/gists{/gist_id}","starred_url":"https://api.github.com/users/cranmer/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/cranmer/subscriptions","organizations_url":"https://api.github.com/users/cranmer/orgs","repos_url":"https://api.github.com/users/cranmer/repos","events_url":"https://api.github.com/users/cranmer/events{/privacy}","received_events_url":"https://api.github.com/users/cranmer/received_events","type":"User","site_admin":false},"created_at":"2016-03-02T23:38:51Z","updated_at":"2016-03-02T23:38:51Z","body":"Thanks! Do you have the Poisson terms for extended likelihood fits?\n\nSeems like a good idea to explicitly treat a joint distribution of (x,y)\ndifferently than two extended likelihoods on p(X) and p(y).\nOn Wed, Mar 2, 2016 at 5:58 PM Chris Burr <notifications@github.com> wrote:\n\n> Just to add, you can see this gist\n> <https://gist.github.com/chrisburr/84774a42b2035cf2fe5d> for a jupyter\n> notebook I made earlier today.\n>\n> —\n> Reply to this email directly or view it on GitHub\n> <https://github.com/tensorprob/tensorprob/issues/59#issuecomment-191481472>\n> .\n>\n"}}