Bayesian statistics

Related lectures (31)

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Explores Bayesian inference, knapsack problem, and prediction using Markov Chain Monte Carlo methods.

Explores the transition from sparse to dense graphs and their real-world applications.

Explores model selection using AIC and BIC criteria, addressing different questions and the importance of sparsity in selecting the best model.

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

Explores Bayesian methods in choice modeling for personalized menu optimization and individual choice prediction.

Explores linear regression fundamentals, emphasizing the importance of regularization techniques to enhance model performance.

Covers the fundamentals of Bayesian estimation, focusing on the application of Bayes' Theorem in scalar estimation.

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

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

Explores Markov chains, transition matrices, and Bayesian statistics in stochastic simulation.