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BDA 2019 Lecture 6.2 probabilistic programming and Stan
Уроки29 уроков
- 1BDA course 1.1 Introduction to uncertainty and modelling
- 2BDA course 1.2 Introduction to the course contents
- 3BDA 2019 Lecture 2.1 Bayesian inference, observation model, likelihood, posterior, and binomial
- 4BDA 2019 Lecture 2.2 priors and prior information, and one parameter normal model
- 5BDA course 2 extra: likelihood, normalization, density, model M
- 6BDA 2019 Lecture 3 on multiparameter models. joint, marginal and conditional distribution, normal
- 7BDA 2019 Lecture 4.1 numerical issues, Monte Carlo, how many simulation draws are needed, ...
- 8BDA 2019 Lecture 4.2 direct simulation, curse of dimensionality, rejection and importance sampling
- 9BDA 2019 Lecture 5.1 Markov chain Monte Carlo, Gibbs sampling, and Metropolis algorithm
- 10BDA 2019 Lecture 5.2 warm up, convergence diagnostics, R-hat, and effective sample size
- 11BDA 2019 Lecture 6.1 HMC, NUTS, dynamic HMC, and HMC specific convergence diagnostics
- 12BDA 2019 Lecture 6.2 probabilistic programming and Stan
- 13Stan and probabilistic programming introduction
- 14BDA 2019 Lecture 7.1 hierarchical models
- 15BDA 2019 Lecture 7.2 exchangeability
- 16BDA 2019 Lecture 8.1 model checking
- 17BDA 2019 Lecture 8.2 cross-validation part 1
- 18BDA 2019 Lecture 9.1 PSIS-LOO and K-fold cross-validation
- 19BDA 2019 Lecture 9.2 model comparison and selection
- 20BDA 2019 Lecture 9.3 extra lecture on projection predictive variable selection
- 21BDA 2019 Lecture 10.1 Decision analysis
- 22BDA 2019 Lecture 11.1 Normal approximation, Laplace approximation.
- 23BDA 2019 Lecture 11.2 Large sample theory and counter examples
- 24BDA 2019 Lecture 12.1 Frequency evaluation, hypothesis testing, and variable selection
- 25BDA 2019 Lecture 12.2 Summary of modeling data collection, linear models, Lasso, and Gaussian proces
- 26BDA course 2.1 Observation model, likelihood, posterior, and binomial model
- 27BDA course 2.2 Predictive distribution and benefit of integration
- 28BDA course 2.3 Priors and prior information
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