{"action":"created","issue":{"url":"https://api.github.com/repos/SheffieldML/GPy/issues/457","repository_url":"https://api.github.com/repos/SheffieldML/GPy","labels_url":"https://api.github.com/repos/SheffieldML/GPy/issues/457/labels{/name}","comments_url":"https://api.github.com/repos/SheffieldML/GPy/issues/457/comments","events_url":"https://api.github.com/repos/SheffieldML/GPy/issues/457/events","html_url":"https://github.com/SheffieldML/GPy/issues/457","id":188578071,"number":457,"title":"Using Bayesian optimization to fit the parameters of a GP","user":{"login":"achapchap","id":13765435,"avatar_url":"https://avatars.githubusercontent.com/u/13765435?v=3","gravatar_id":"","url":"https://api.github.com/users/achapchap","html_url":"https://github.com/achapchap","followers_url":"https://api.github.com/users/achapchap/followers","following_url":"https://api.github.com/users/achapchap/following{/other_user}","gists_url":"https://api.github.com/users/achapchap/gists{/gist_id}","starred_url":"https://api.github.com/users/achapchap/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/achapchap/subscriptions","organizations_url":"https://api.github.com/users/achapchap/orgs","repos_url":"https://api.github.com/users/achapchap/repos","events_url":"https://api.github.com/users/achapchap/events{/privacy}","received_events_url":"https://api.github.com/users/achapchap/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2016-11-10T18:04:15Z","updated_at":"2016-11-17T11:27:10Z","closed_at":null,"body":"Hi,\r\n \r\nI wonder whether it would be possible to use a GPy in order  to fit a GP (quad GP  ?) to the negative log likehood of another GP (x = hyper parameters and y =nlml) and then try to find the hyper parameters by doing BO. \r\n\r\nThe closest approach I have found was Bayesian quadrature, but if I understood it correctly it tries to model the whole posterior rather than nlml . In addition in this case, should one try to minimize the more conventional acquistions (say EI and UCB) or perhaps try to minimize uncertainty instead  (say the variance of the quad GP)  ? \r\n\r\nThanks very much, \r\n\r\n"},"comment":{"url":"https://api.github.com/repos/SheffieldML/GPy/issues/comments/261223435","html_url":"https://github.com/SheffieldML/GPy/issues/457#issuecomment-261223435","issue_url":"https://api.github.com/repos/SheffieldML/GPy/issues/457","id":261223435,"user":{"login":"achapchap","id":13765435,"avatar_url":"https://avatars.githubusercontent.com/u/13765435?v=3","gravatar_id":"","url":"https://api.github.com/users/achapchap","html_url":"https://github.com/achapchap","followers_url":"https://api.github.com/users/achapchap/followers","following_url":"https://api.github.com/users/achapchap/following{/other_user}","gists_url":"https://api.github.com/users/achapchap/gists{/gist_id}","starred_url":"https://api.github.com/users/achapchap/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/achapchap/subscriptions","organizations_url":"https://api.github.com/users/achapchap/orgs","repos_url":"https://api.github.com/users/achapchap/repos","events_url":"https://api.github.com/users/achapchap/events{/privacy}","received_events_url":"https://api.github.com/users/achapchap/received_events","type":"User","site_admin":false},"created_at":"2016-11-17T11:27:10Z","updated_at":"2016-11-17T11:27:10Z","body":"Thanks very much, very helpful comments. \r\nThe main reason I am considering doing this is to try to address the variance of negative log likehood as well and hopefully try to avoid regions with high variance. Intuitively I am temped to think that regions where log likehood has a large variance are prone to overfitting. Do you think this could make sense ? \r\n\r\nStill regarding the GPy, I noticed that the Gabor kernel does not seem to be implemented. However, it seems to be possible to construct it by taking the product of the periodic kernel (cos(r) ) and the exponential kernel. Is that the case or should I try to construct the Gabor kernel following the GPy documentation ?  "}}
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