{"action":"created","issue":{"url":"https://api.github.com/repos/clberube/BISIP/issues/3","repository_url":"https://api.github.com/repos/clberube/BISIP","labels_url":"https://api.github.com/repos/clberube/BISIP/issues/3/labels{/name}","comments_url":"https://api.github.com/repos/clberube/BISIP/issues/3/comments","events_url":"https://api.github.com/repos/clberube/BISIP/issues/3/events","html_url":"https://github.com/clberube/BISIP/issues/3","id":306382211,"number":3,"title":"Misfit Computing","user":{"login":"MariusLTU","id":37534704,"avatar_url":"https://avatars2.githubusercontent.com/u/37534704?v=4","gravatar_id":"","url":"https://api.github.com/users/MariusLTU","html_url":"https://github.com/MariusLTU","followers_url":"https://api.github.com/users/MariusLTU/followers","following_url":"https://api.github.com/users/MariusLTU/following{/other_user}","gists_url":"https://api.github.com/users/MariusLTU/gists{/gist_id}","starred_url":"https://api.github.com/users/MariusLTU/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/MariusLTU/subscriptions","organizations_url":"https://api.github.com/users/MariusLTU/orgs","repos_url":"https://api.github.com/users/MariusLTU/repos","events_url":"https://api.github.com/users/MariusLTU/events{/privacy}","received_events_url":"https://api.github.com/users/MariusLTU/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2018-03-19T09:12:56Z","updated_at":"2018-03-20T15:28:20Z","closed_at":null,"author_association":"NONE","body":"Is it possible to have a function that calculates the misfit of the data once the model has be drawn?"},"comment":{"url":"https://api.github.com/repos/clberube/BISIP/issues/comments/374640309","html_url":"https://github.com/clberube/BISIP/issues/3#issuecomment-374640309","issue_url":"https://api.github.com/repos/clberube/BISIP/issues/3","id":374640309,"user":{"login":"clberube","id":20193582,"avatar_url":"https://avatars3.githubusercontent.com/u/20193582?v=4","gravatar_id":"","url":"https://api.github.com/users/clberube","html_url":"https://github.com/clberube","followers_url":"https://api.github.com/users/clberube/followers","following_url":"https://api.github.com/users/clberube/following{/other_user}","gists_url":"https://api.github.com/users/clberube/gists{/gist_id}","starred_url":"https://api.github.com/users/clberube/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/clberube/subscriptions","organizations_url":"https://api.github.com/users/clberube/orgs","repos_url":"https://api.github.com/users/clberube/repos","events_url":"https://api.github.com/users/clberube/events{/privacy}","received_events_url":"https://api.github.com/users/clberube/received_events","type":"User","site_admin":false},"created_at":"2018-03-20T15:28:20Z","updated_at":"2018-03-20T15:28:20Z","author_association":"OWNER","body":"As any statistician will tell you, with MCMC there are no way to be 100% sure that the average model found is the global optimum. However if you always converge to a similar model there are good changes that this model is a good one. Least-squares fitting doesn't make much sense for a probabilistic inversion. At each iteration the model quality is evaluated using the likelihood function. Think of it as a probabilistic misfit function.\r\n\r\nWhat the MCMC algorithm does is look at the likelihood for many iterations and change the model to find a multitude of optimal results. Near the end of the simulation, if it is let run long enough, all iterations are more or less equally probable. Therefore it does not make sense to chose only 1 of them. The spread of those probable models depends only on the data noise. What makes sense is considering all iterations using basic statistics such as mean, SD, 95% HPD.\r\n\r\nWhat you're asking is possible, and bisip can compute a deterministic misfit for each iteration. This information is easiest to extract when working from a python console. Using the GUI will always be much less flexible and limited to whatever free time I have to develop it.\r\n\r\nI'll see if I can add a switch to add this information automatically in the CSV, but I don't like the idea of generating very large files automatically. \r\n\r\nIn summary: \r\n-Use a simple least-squares algorithm if looking for only 1 solution and no uncertainty. \r\n-Use the MCMC algorithm in bisip if looking for what solution on average is the best and what the uncertainty of this solution is."}}
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