{"action":"created","issue":{"url":"https://api.github.com/repos/blei-lab/edward/issues/211","repository_url":"https://api.github.com/repos/blei-lab/edward","labels_url":"https://api.github.com/repos/blei-lab/edward/issues/211/labels{/name}","comments_url":"https://api.github.com/repos/blei-lab/edward/issues/211/comments","events_url":"https://api.github.com/repos/blei-lab/edward/issues/211/events","html_url":"https://github.com/blei-lab/edward/issues/211","id":170429682,"number":211,"title":"Time-series models in edward","user":{"login":"fmpr","id":15234576,"avatar_url":"https://avatars.githubusercontent.com/u/15234576?v=3","gravatar_id":"","url":"https://api.github.com/users/fmpr","html_url":"https://github.com/fmpr","followers_url":"https://api.github.com/users/fmpr/followers","following_url":"https://api.github.com/users/fmpr/following{/other_user}","gists_url":"https://api.github.com/users/fmpr/gists{/gist_id}","starred_url":"https://api.github.com/users/fmpr/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/fmpr/subscriptions","organizations_url":"https://api.github.com/users/fmpr/orgs","repos_url":"https://api.github.com/users/fmpr/repos","events_url":"https://api.github.com/users/fmpr/events{/privacy}","received_events_url":"https://api.github.com/users/fmpr/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2016-08-10T14:22:36Z","updated_at":"2016-11-07T11:17:59Z","closed_at":null,"body":"Hi,\n\nI'm interested in doing some time-series state-space modelling in edward. So I started implementing a simple autoregressive model AR(k) with a toy dataset. I started out with fixed values for the observation noise and for the variance in the latent process, and everything seemed to work great and fast (code for this example is available here:  [ARk_fixed_noises.py](https://github.com/fmpr/edward/blob/example/timeseries/examples/ARk_fixed_noises.py)  ).\n\nHowever, if I place Inverse-Gamma priors on these two variances and estimate their distributions, then I need to increase the number of samples significantly in order to obtain any decent results for the inferred states, and then MFVI (naturally) becomes quite slow even for this very small toy example (code with InvGamma priors: [ARk_estimate_obs_noise.py](https://github.com/fmpr/edward/blob/example/timeseries/examples/ARk_estimate_obs_noise.py)  and [ARk_estimate_both.py](https://github.com/fmpr/edward/blob/example/timeseries/examples/ARk_estimate_both.py)  ).\n\nSo may questions are: \n- Did anyone implemented time-series models in edward before?\n- Do you have any intuition why introducing these two univariate Inverse-Gamma priors can make such a huge difference in the behavior of the inference algorithm? (I have similar models implemented in Stan with ADVI and inference is fast...). Am I doing something wrong? Any suggestions are welcome :-)\n"},"comment":{"url":"https://api.github.com/repos/blei-lab/edward/issues/comments/258810167","html_url":"https://github.com/blei-lab/edward/issues/211#issuecomment-258810167","issue_url":"https://api.github.com/repos/blei-lab/edward/issues/211","id":258810167,"user":{"login":"maedoc","id":5189886,"avatar_url":"https://avatars.githubusercontent.com/u/5189886?v=3","gravatar_id":"","url":"https://api.github.com/users/maedoc","html_url":"https://github.com/maedoc","followers_url":"https://api.github.com/users/maedoc/followers","following_url":"https://api.github.com/users/maedoc/following{/other_user}","gists_url":"https://api.github.com/users/maedoc/gists{/gist_id}","starred_url":"https://api.github.com/users/maedoc/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/maedoc/subscriptions","organizations_url":"https://api.github.com/users/maedoc/orgs","repos_url":"https://api.github.com/users/maedoc/repos","events_url":"https://api.github.com/users/maedoc/events{/privacy}","received_events_url":"https://api.github.com/users/maedoc/received_events","type":"User","site_admin":false},"created_at":"2016-11-07T11:17:59Z","updated_at":"2016-11-07T11:17:59Z","body":"@fmpr thanks for posting this code, exactly what I was looking for. What's your end goal? Just AR? I recently added a Euler-Maruyama scheme PyMC3 and will try to do something similar here, if possible. Would there be interest in contributing such support for SDEs in Edward?"}}
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