Lecture
Optimization and Simulation
Description
This lecture covers optimization and simulation techniques such as Metropolis-Hastings, Gibbs sampling, and simulated annealing. It explains how to draw from multivariate distributions and deal with correlations using Markov chains. Examples include bivariate normal distribution and probability density functions.
Instructor
non consectetur deserunt
Incididunt ex ipsum nostrud magna tempor nostrud officia in anim Lorem minim. Est voluptate do pariatur et sit Lorem reprehenderit Lorem in. Ad exercitation laborum officia sunt sit. Cillum laborum aliquip amet mollit. Dolor aliqua ea culpa quis consequat laborum est est officia consequat commodo nulla. Esse labore consectetur do Lorem elit nulla culpa proident minim irure.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related lectures (32)
Simulation & Optimization: Poisson Process & Random Numbers
Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.
Optimization and Simulation
Explores optimization techniques like Metropolis-Hastings and Simulated Annealing through Markov chains and stationary distributions.
Markov Chains and Algorithm Applications
Covers the application of Markov chains and algorithms for function optimization and graph colorings.
Markov Chains and Applications
Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.
Markov Chains: Simulation and Optimization
Explores Markov chains, Metropolis-Hastings, and simulation for optimization purposes, highlighting the significance of ergodicity in efficient variable simulation.