Aki Vehtari
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Bayesian Data Analysis
15 posts · theirs
Lately
BDA course 2 extra: likelihood, normalization, density, model M
BDA 2019 Lecture 7.2 exchangeability
BDA 2019 Lecture 7.1 hierarchical models
BDA 2019 Lecture 6.2 probabilistic programming and Stan
BDA 2019 Lecture 6.1 HMC, NUTS, dynamic HMC, and HMC specific convergence diagnostics
BDA 2019 Lecture 5.2 warm up, convergence diagnostics, R-hat, and effective sample size
BDA 2019 Lecture 5.1 Markov chain Monte Carlo, Gibbs sampling, and Metropolis algorithm
BDA 2019 Lecture 4.2 direct simulation, curse of dimensionality, rejection and importance sampling
BDA 2019 Lecture 4.1 numerical issues, Monte Carlo, how many simulation draws are needed, ...
BDA 2019 Lecture 3 on multiparameter models. joint, marginal and conditional distribution, normal
BDA 2019 Lecture 2.2 priors and prior information, and one parameter normal model
BDA 2019 Lecture 2.1 Bayesian inference, observation model, likelihood, posterior, and binomial
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