PT  - JOURNAL ARTICLE
AU  - Dániel Czégel
AU  - Hamza Giaffar
AU  - István Zachar
AU  - Eörs Szathmáry
TI  - Evolutionary implementation of Bayesian computations
AID  - 10.1101/685842
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 685842
4099  - http://biorxiv.org/content/early/2019/06/28/685842.short
4100  - http://biorxiv.org/content/early/2019/06/28/685842.full
AB  - A wide variety of human and non-human behavior is computationally well accounted for by probabilistic generative models, formalized consistently in a Bayesian framework. Recently, it has been suggested that another family of adaptive systems, namely, those governed by Darwinian evolutionary dynamics, are capable of implementing building blocks of Bayesian computations. These algorithmic similarities rely on the analogous competition dynamics of generative models and of Darwinian replicators to fit possibly high-dimensional and stochastic environments. Identified computational building blocks include Bayesian update over a single variable and replicator dynamics, transition between hidden states and mutation, and Bayesian inference in hierarchical models and multilevel selection. Here we provide a coherent mathematical discussion of these observations in terms of Bayesian graphical models and a step-by-step introduction to their evolutionary interpretation. We also extend existing results by adding two missing components: a correspondence between likelihood optimization and phenotypic adaptation, and between expectation-maximization-like dynamics in mixture models and ecological competition. These correspondences suggest a deeper algorithmic analogy between evolutionary dynamics and statistical learning, pointing towards a unified computational understanding of mechanisms Nature invented to adapt to high-dimensional and uncertain environments.