The essays in Bayesian models of cognition, focus on reverse-engineering the mind by applying probabilistic methods. The sources introduce the Bayesian approach to cognitive science as a framework for solving the problem of induction, explaining how the mind goes beyond observed data through abstract knowledge, priors, or inductive bias. The material explores foundational concepts in Bayesian inference, such as Bayes' rule and model selection, and connects them to more complex structures like graphical models and hierarchical Bayesian models for representing causal and relational knowledge. Finally, the text transitions to discussing advanced topics including resource-rational analysis, intuitive physics, language processing, and the expressive power of Probabilistic Programming Languages (PPLs), suggesting that these mathematical tools offer a unified understanding of human intelligence and decision-making.

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