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How can we apply Bayes theorem on histogram ?

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closed as not a real question by Yemon Choi, Qiaochu Yuan, Douglas Zare, algori, Steven Landsburg Oct 1 '12 at 1:21

It's difficult to tell what is being asked here. This question is ambiguous, vague, incomplete, overly broad, or rhetorical and cannot be reasonably answered in its current form. For help clarifying this question so that it can be reopened, visit the help center.If this question can be reworded to fit the rules in the help center, please edit the question.

Search "Dirichlet processes" or "Bayesian density estimation". – R Hahn Oct 1 '12 at 0:49
Apply the theorem to do what? – Yemon Choi Oct 1 '12 at 0:55
I am looking specially for histogram . but i did not find that in the questions related to Bayesian density estimation. – tina123 Oct 1 '12 at 0:55
@yemon Well , using Bayesian formula , we can show that posterior probability which is proportional to the density value. So i am interested in Bayesian formula on histogram . – tina123 Oct 1 '12 at 0:58
Dear tina123, this question needs some amending. Please read the howtoask page and then edit your question. – Theo Johnson-Freyd Oct 1 '12 at 1:20
up vote 1 down vote accepted

You may treat the histogram as observations arising from a multinomial distribution. The conjugate prior for a multinomial likelihood is a Dirichlet distribution. If you want to allow the number of bins to grow as you collect more data, this leads to a Dirichlet process prior, as I mentioned in my early comment.

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