Inference Problems & Spin Glass Game

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Related lectures (31)

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Monte Carlo: Markov Chains

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

Probability and Statistics: Independence and Conditional Probability

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Exponential Family

Covers the properties of the exponential family and the estimation of parameters.

Relevance Vector Machine: Addressing SVM Shortcomings

Introduces the Relevance Vector Machine as a sparse solution to SVM shortcomings.

Bayesian Extremes: MCMC Analysis

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

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Statistical Theory: Inference and Optimality

Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.

Statistical Inference: Exponential Families and Likelihoods

Explores exponential families, likelihood functions, and model regularity in statistical inference.

Densities and Bayesian Inference: Decision Rules and Bayes Law

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

Extreme Value Analysis: Statistical Consequences

Explores the statistical consequences of extreme value analysis and implications of model mis-specification.