Simulation & Optimization: Poisson Process & Random Numbers

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 1 of 3

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Continuous Random Variables

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Optimization and Simulation: Introduction to Simulation

Introduces state-of-the-art methods in optimization and simulation, covering topics like statistical analysis, variance reduction, and simulation projects.

Optimization and Simulation

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

Probability and Statistics: Fundamental Theorems

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Probability and Statistics

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.

Optimization and Simulation

Explores statistical analysis, mean square error, and bootstrapping methods in optimization and simulation.

Probability & Stochastic Processes

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

Central Limit Theorem

Covers the central limit theorem, showing how random processes converge to a normal distribution.