Concept

Rejection sampling

Related lectures (32)

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

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

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

Covers the basics of Markov chains and their algorithmic applications.

Covers the fundamentals of Markov chains and their applications in algorithms, focusing on proper coloring and the Metropolis algorithm.

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

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

Explores advanced Acceptance-Rejection methods, sampling from normal distribution, and multivariate random variable generation.

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

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

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

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

Covers the implementation of Markov Chain Monte Carlo (MCMC) with the Metropolis algorithm for sampling from posterior distributions.

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

Explores methods for generating nonuniform distributions, including the rejection method of von Neumann and the central limit theorem.

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

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

Explores sampling algorithms for constrained systems, emphasizing the work of Benedict Leimkuhler from the University of Edinburgh.

Offers a crash course on generative models, covering exponential families, sampling methods, and Metropolis algorithm.

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.