Lectures related to Gibbs sampling

Covers the concept of Gibbs sampling and its application in simulated annealing.

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

Optimization and Simulation

Covers optimization and simulation techniques for drawing from multivariate distributions and dealing with correlations.

Network Sampling: Consistency, Models, and Dynamics

Explores network sampling consistency, models, and graph dynamics in real-life scenarios.

Personalized Menu Optimization

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

Bayesian Estimation: Overview and Examples

Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.

Monte Carlo Markov Chains

Covers Monte Carlo Markov Chains and sampling algorithms for iterative trial configurations.

Correlated and Uncorrelated Sampling

Explains correlated and uncorrelated sampling for generating random variables with given weight functions.

Monte Carlo Simulation: Lennard-Jones Liquid

Covers the Monte Carlo simulation of a Lennard-Jones liquid.

Diffusion Models

Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.

Sampling Distributions: Theory and Applications

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Monte Carlo Integration: Correlated Sampling

Covers Monte Carlo integration through correlated sampling and error scaling for grid integration.

Markov Chain Monte Carlo: Rejection Sampling

Explores rejection sampling for generating sample values from a target distribution, along with Bayesian inference using MCMC.

Explores correlated sampling, weight functions, Markovian chains, ergodicity, and walkers.

Probability & Stochastic Processes

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Errors in Correlated Sampling

Explains errors in correlated and uncorrelated sampling, correlation function, time, and blocking analysis.

Optimization and Simulation: Bayesian Inference

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

Sampling Theory: Statistics for Mathematicians

Covers the theory of sampling, focusing on statistics for mathematicians.

Sampling Distributions: Understanding Ancillary Statistics

Explores ancillary statistics, sufficiency, and minimally sufficient statistics in sampling distributions.

Statistical Analysis: Data Exploration and Inference

Covers statistical analysis, emphasizing data exploration and inference to quantify uncertainty and draw conclusions.