Bayesian Inference: Posterior Computation

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 2 of 3

Bayesian Extremes: Markov Chain Monte Carlo

Explores Bayesian techniques for extreme value problems, including Markov Chain Monte Carlo and Bayesian inference, emphasizing the importance of prior information and the use of graphs.

Bayesian Inference: Gaussian Variables

Explores Bayesian inference for Gaussian random variables, covering joint distribution, marginal pdfs, and the Bayes classifier.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Statistical Physics of Computation: Insights and Applications

Explores the application of statistical physics in computational problems, covering topics such as Bayesian inference, mean-field spin glass models, and compressed sensing.

Probability and Statistics

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Inference of Reaction Kinetics

Focuses on the inference of reaction kinetics in combustion, covering rules inference, sensitivity analysis, and Bayesian inference.

Fundamentals of Inference and Learning

Covers the theory of statistics, inference, and machine learning with practical exercises in Python.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Bayesian Inference: Precision in Gaussian Model

Explores Bayesian inference for precision in the Gaussian model with known mean, using a Gamma prior and discussing subjective vs objective priors.

Linear Regression: Regularization Overview

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