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Many of the mathematical frameworks describing natural selection areequivalent to Bayes Theorem, also known as Bayesian updating. By definition, aprocess of Bayesian Inference is one which involves a Bayesian update, so wemay conclude that these frameworks describe natural selection as a process ofBayesian inference. Thus natural selection serves as a counter example to awidely-held interpretation that restricts Bayesian Inference to human mentalprocesses (including the endeavors of statisticians). As Bayesian inference canalways be cast in terms of (variational) free energy minimization, naturalselection can be viewed as comprising two components: a generative model of an"experiment" in the external world environment, and the results of that"experiment" or the "surprise" entailed by predicted and actual outcomes of the"experiment". Minimization of free energy implies that the implicit measure of"surprise" experienced serves to update the generative model in a Bayesianmanner. This description closely accords with the mechanisms of generalizedDarwinian process proposed both by Dawkins, in terms of replicators andvehicles, and Campbell, in terms of inferential systems. Bayesian inference isan algorithm for the accumulation of evidence-based knowledge. This algorithmis now seen to operate over a wide range of evolutionary processes, includingnatural selection, the evolution of mental models and cultural evolutionaryprocesses, notably including science itself. The variational principle of freeenergy minimization may thus serve as a unifying mathematical framework foruniversal Darwinism, the study of evolutionary processes operating throughoutnature.

Universal Darwinism as a process of Bayesian inference