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Concept

Rejection sampling

Related lectures (32)

Gibbs Sampling: Simulated Annealing

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

Markov Chains: Applications and Sampling Methods

Covers the basics of Markov chains and their algorithmic applications.

Markov Chains and Algorithm Applications

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

Correlated and Uncorrelated Sampling

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

Diffusion Models

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

Monte Carlo Simulation: Lennard-Jones Liquid

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

Monte Carlo Integration: Correlated Sampling

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

Optimization and Simulation

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

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

Sampling Distributions: Theory and Applications

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

Nonuniform Distributions: Generation Methods

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

Errors in Correlated Sampling

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

Expectation Maximization and Clustering

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

Sampling Algorithms for Constrained Systems

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

Generative Models: Crash Course

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

Deep Learning Modus Operandi

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

Convergence of Langevin Monte Carlo: Tail Growth and Smoothness

Explores the convergence of Langevin Monte Carlo algorithms under different growth rates and smoothness conditions, emphasizing fast convergence for a wide class of potentials.

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.

Sampling Distributions: Understanding Ancillary Statistics

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

Monte Carlo: Optimization and Estimation

Explores optimization and estimation in Monte Carlo methods, emphasizing Bayes-optimal groups and estimators.