Alex Fengler explains HSSM, amortized inference, and likelihood approximation networks for cognitive process models like the drift diffusion model

Andrew Gelman, Aki Vehtari, and Richard McElreath discuss their new book on Bayesian workflow, reverse Bayes and hierarchical pooling.

Adrian Seyboldt and Eliot Carlson explain Fisher divergence preconditioning, mass matrix adaptation, normalizing flows, and benchmarking samplers in Nutpie.

Stefan Radev explains how simulations improve Bayesian workflows, how to do cheap sensitivity and multiverse analysis, and where BayesFlow is headed next.

Thomas Pinder discusses Bayesian causal inference and Gaussian processes. Explore synthetic control and diff-in-diff for industry

A set of skills to call your agent Bayes. Thomas Bayes. - Learning-Bayesian-Statistics/baygent-skills

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