{"action":"closed","issue":{"url":"https://api.github.com/repos/GPflow/GPflow/issues/671","repository_url":"https://api.github.com/repos/GPflow/GPflow","labels_url":"https://api.github.com/repos/GPflow/GPflow/issues/671/labels{/name}","comments_url":"https://api.github.com/repos/GPflow/GPflow/issues/671/comments","events_url":"https://api.github.com/repos/GPflow/GPflow/issues/671/events","html_url":"https://github.com/GPflow/GPflow/issues/671","id":303389295,"number":671,"title":"GPFlow with missing values / sparse observation matrix?","user":{"login":"charleshamesse","id":10261071,"avatar_url":"https://avatars2.githubusercontent.com/u/10261071?v=4","gravatar_id":"","url":"https://api.github.com/users/charleshamesse","html_url":"https://github.com/charleshamesse","followers_url":"https://api.github.com/users/charleshamesse/followers","following_url":"https://api.github.com/users/charleshamesse/following{/other_user}","gists_url":"https://api.github.com/users/charleshamesse/gists{/gist_id}","starred_url":"https://api.github.com/users/charleshamesse/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/charleshamesse/subscriptions","organizations_url":"https://api.github.com/users/charleshamesse/orgs","repos_url":"https://api.github.com/users/charleshamesse/repos","events_url":"https://api.github.com/users/charleshamesse/events{/privacy}","received_events_url":"https://api.github.com/users/charleshamesse/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2018-03-08T08:03:18Z","updated_at":"2018-03-09T14:25:43Z","closed_at":"2018-03-09T14:25:43Z","author_association":"NONE","body":"Hi,\r\n\r\nI have some X (NxM, dense) and some Y (NxP, sparse, lot of missing values currently as nan). My goal is to recover the missing values in Y. I've been skimming through notebooks and examples here but can't seem to find resources on how to use GPFlow to model this. \r\n\r\nCould anyone let me know the way to go for this? Any help appreciated. Thanks!"}}
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