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Dr. Thomas Parr - Active Inference Book
Machine Learning Street Talk (MLST)
Bayesian vs Maximum Likelihood Estimation in Active Inference
In active inference, a Bayesian approach is favored over maximum likelihood estimation due to the necessity of maintaining plausibility by considering prior information. Maximum likelihood estimation ignores prior probabilities, leading to potentially implausible solutions in cases of inverse problems with multiple plausible explanations. Furthermore, maximum likelihood estimation provides point estimates without considering uncertainty, which can result in overfitting and misclassifications. From a technical perspective, Bayesian inference accounts for complexity and accuracy through free energy, unlike maximum likelihood approaches that discard complexity considerations, leading to a more nuanced and prioritized explanation in Bayesian frameworks.
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