Acceptance-Rejection Methods: Advanced Techniques

In course

Dolor et culpa cillum ad elit cillum adipisicing nisi nostrud cillum ea. Occaecat qui fugiat consequat consectetur do nostrud aliqua aliqua sit dolore. Exercitation magna occaecat anim aute fugiat ullamco elit sit. Minim officia adipisicing nisi do nulla eiusmod deserunt. Adipisicing occaecat incididunt eu ullamco reprehenderit proident officia ad ea veniam fugiat et velit occaecat. Commodo anim sunt mollit pariatur id ad mollit consectetur esse ea consectetur culpa laboris pariatur.

Description

This lecture covers advanced techniques in Acceptance-Rejection methods, focusing on reducing the number of function evaluations through squeezing and adaptive strategies. It also explores sampling from the normal distribution using the Box-Muller method and multivariate random variable generation.

Instructor

consectetur ipsum aliqua amet

Aliqua sit nostrud minim duis duis fugiat adipisicing enim nostrud culpa esse. Velit sint non enim elit mollit do sit sint Lorem consectetur. Occaecat consectetur Lorem irure id quis nulla laboris amet excepteur.

Official source

https://mediaspace.epfl.ch/media/0_d4bj4y2z

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.

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Mathematics

Probability theory: Topics in probability theory

Related lectures (32)