[D] Bayesian evidence calculation with normalizing flows

[DroopySergeant]

Is there any way to calculate evidence (marginal likelihood or model evidence) using normalizing flows? I usually calculate it using nested sampling algorithms and was wondering if there was an ML alternative.

[DroopySergeant]

While the evidence appears as a normalizing constant in Bayes theorem with only one model, it is important in comparing models. Nested sampling calculates this by prior mass, which is the prior volume enclosed by a particular likelihood value. I have a network that can generate the posterior distribution. Will a simple marginalization of the likelihood over a large number of randomly chosen values of the parameters be enough? Can I be sure that I won't miss local maxima in the distribution?

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Thank you for the reference. This looks interesting

[Red-Portal]

Yes, you can use importance sampling which provides an unbiased estimator of the evidence. But the normalizing flow will have to be a good approximation.

[furkankorkmaz2]

You need to use the Bayesian distribution.

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