Important Sampling: Monte Carlo Estimation

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (29)

Page 1 of 3

Statistical Theory: Fundamentals

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Spin Glasses and Bayesian Estimation

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Probability & Stochastic Processes

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

Detection & Estimation

Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.

Statistical Models and Parameter Estimation

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Jacamar Data Analysis

Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.

Density of States and Bayesian Inference in Computational Mathematics

Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.

Maximum Likelihood Estimation

Covers maximum likelihood estimation, likelihood function, parameter estimation, and hypothesis testing.

Sampling strategies

Explores research process, variable types, causality vs correlation, and sampling strategies.

Distribution Estimation

Covers the estimation of distributions using samples and probability models.