# Tagged Questions

The approach and interpretation of probability associated with Bayes theorem; usually used as opposed to the frequentist approach. It can be seen as an extension of logic that enables reasoning with propositions whose truth or falsity is uncertain. A Bayesian probabilist starts with some prior ...

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### How to derive the posterior predictive distribution?

I often seen the posterior predictive distribution mentioned in the context of machine learning and bayesian inference. The definition is as follows: $p(D'|D) = \int_\theta p(D'|\theta)p(\theta|D)$ ...
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### Find parameters of the posterior Gaussian distribution

The question is to find $p(x|y)$ given that $p(x) \sim \mathcal{N}(\mu, \Sigma)$ and $p(y|x) \sim \mathcal{N}(Ax, \Gamma)$. I do realize that I may just obtain a posterior through application of ...
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### What are the different ways to do a rating in a web application?

I tried to do an example using Bayesian Average in order to find the rating for 3 hotels. Following is my example, Hotel A 3 Votes/ 2 Star/ 1 Star,Rating- 3 Star Hotel B 1 Vote, Rating -5 Star ...
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### Bayesian Analysis and Lindley's paradox?

So I have this problem to solve. Can anyone give me some hints on how to get started ? I have an understanding of conjugate priors and Driac function but have no idea how to apply it here.
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### Bayes network predecessor relation

I have the following Bayes network. I know when "SHOES WET" becomes true, the probability of "GROUND WET" will change. But why will the probability of "RAINING" also change? And how can I ...
I have a group of $n$ events. The successes don't all come in at once, and and I want to try to predict the actual success rate $s$. The number of successes showing in the system at any given time can ...