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Bayesian Data Analysis

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BDA course 2 extra: likelihood, normalization, density, model M

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BDA 2019 Lecture 7.2 exchangeability

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BDA 2019 Lecture 7.1 hierarchical models

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BDA 2019 Lecture 6.2 probabilistic programming and Stan

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BDA 2019 Lecture 6.1 HMC, NUTS, dynamic HMC, and HMC specific convergence diagnostics

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BDA 2019 Lecture 5.2 warm up, convergence diagnostics, R-hat, and effective sample size

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BDA 2019 Lecture 5.1 Markov chain Monte Carlo, Gibbs sampling, and Metropolis algorithm

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BDA 2019 Lecture 4.2 direct simulation, curse of dimensionality, rejection and importance sampling

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BDA 2019 Lecture 4.1 numerical issues, Monte Carlo, how many simulation draws are needed, ...

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BDA 2019 Lecture 3 on multiparameter models. joint, marginal and conditional distribution, normal

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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

BDA course 1.2 Introduction to the course contents

Stan and probabilistic programming introduction

BDA course 1.1 Introduction to uncertainty and modelling