Posts tagged model comparison

Bayes Factors and Marginal Likelihood

The “Bayesian way” to compare models is to compute the marginal likelihood of each model \(p(y \mid M_k)\), i.e. the probability of the observed data \(y\) given the \(M_k\) model. This quantity, the marginal likelihood, is just the normalizing constant of Bayes’ theorem. We can see this if we write Bayes’ theorem and make explicit the fact that all inferences are model-dependant.

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GLM: Model Selection

A fairly minimal reproducible example of Model Selection using WAIC, and LOO as currently implemented in PyMC3.

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Bayesian Estimation Supersedes the T-Test

Non-consecutive header level increase; H1 to H3 [myst.header]

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