Bayesian Inference: Posterior Computation

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Related lectures (30)

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Monte Carlo: Markov Chains

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Dirichlet-Multinomial Model

Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.

Estimation & Bayesian Inference

Covers demousing, estimation, Bayesian inference, likelihood, AWGN, and more.

Bayesian Extremes: MCMC Analysis

Explores Bayesian techniques for extreme value problems, focusing on MCMC analysis and the importance of proper prior information.

Text Models: Word Embeddings and Topic Models

Explores word embeddings, topic models, Word2vec, Bayesian Networks, and inference methods like Gibbs sampling.

Bayesian Inference: Optimal Decisions

Explores Bayesian inference for optimal decision-making in hypothesis testing scenarios.

Densities and Bayesian Inference: Decision Rules and Bayes Law

Covers classification concepts, medical screening tests, and decision rules.

Modern Regression: Smoothing and Modelling Choices

Explores roughness penalty, band matrices, and Bayesian inference in regression smoothing.

Linear Combinations: Moment-Generating Functions

Explores moment-generating functions, linear combinations, and normality of random variables.

Statistical Consequences of Clustering

Covers the statistical consequences of clustering and the complexities of return level estimation in extreme events.