Spin Glasses and Bayesian Estimation

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Related lectures (30)

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Detection & Estimation

Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.

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.

Introduction to Inference

Covers the basics of probability theory, random variables, joint probability, and inference.

Probability and Statistics: Basics and Applications

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Expectation Maximization and Clustering

Covers the Expectation Maximization algorithm and clustering techniques, focusing on Gibbs Sampling and detailed balance.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Jacamar Data Analysis

Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.

Maximum Likelihood Estimation

Covers maximum likelihood estimation, likelihood function, parameter estimation, and hypothesis testing.

Statistical Theory: Cramér-Rao Bound & Hypothesis Testing

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.

Monte Carlo: Markov Chains

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