#StackBounty: #estimation #maximum-likelihood #pdf How to fit a unnormalized parametric distribution with MLE?

Bounty: 50

I’m somewhat familiar with parametric estimation using MLE in the context of fitting the parameters of a distribution given a sample. Is there a way of generalizing this approach to unnormalized models (for instance, neural networks)? Na├»vely maximizing the predicted log-likelihood would simply lead to the model predicting high values everywhere.


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