Lecture

Stochastic Simulation: Metropolis-Hastings Algorithm

In course
DEMO: irure id deserunt
Laboris eu cupidatat ea excepteur velit dolore. Quis aliquip labore ut in minim dolor anim non quis tempor proident eu. Non nisi sint mollit fugiat deserunt eiusmod. Elit tempor labore est eu ad. Ex nostrud nulla velit voluptate elit. Mollit amet aliquip fugiat cupidatat et non et laborum excepteur enim fugiat. Nulla Lorem sunt consequat esse eiusmod.
Login to see this section
Description

This lecture covers the Metropolis-Hastings algorithm for stochastic simulation, focusing on the acceptance rate, target measure, and proposal distribution. It explains the algorithm's steps, including generating samples and defining the acceptance rate. The lecture also discusses the convergence of the algorithm and the importance of the proposal distribution.

Instructor
ex ut voluptate do
Eiusmod anim est velit deserunt commodo dolor laboris ut laboris sit voluptate sint est. Duis sit eiusmod ea ex duis labore nisi laborum aliquip do duis anim. Laborum adipisicing labore ad incididunt et. Eiusmod velit id aute Lorem velit pariatur reprehenderit officia.
Login to see this section
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 (28)
Quantum Random Number Generation
Explores quantum random number generation, discussing the challenges and implementations of generating good randomness using quantum devices.
Joint Equidistribution of CM Points
Covers the joint equidistribution of CM points and the ergodic decomposition theorem in compact abelian groups.
Determinantal Point Processes and Extrapolation
Covers determinantal point processes, sine-process, and their extrapolation in different spaces.
Measure Spaces: Integration and Inequalities
Covers measure spaces, integration, Radon-Nikodym property, and inequalities like Jensen, Hölder, and Minkowski.
Probability Theory: Integration and Convergence
Covers topics in probability theory, focusing on uniform integrability and convergence theorems.
Show more

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.