Lecture
Conditional Gaussian Generation
In course
DEMO: nisi consectetur nulla
Ex magna elit incididunt do officia esse eiusmod cupidatat enim tempor. Cupidatat qui amet sint nulla ea aute. Elit sint occaecat sint fugiat adipisicing id enim dolor voluptate. Eu nisi cillum cillum fugiat sint amet exercitation excepteur ea. Magna dolore incididunt amet ex incididunt exercitation dolore ullamco minim laboris qui. Irure in elit sint fugiat non eiusmod ut dolore labore non. Eiusmod et dolore ut aute eu consequat esse.
Description
This lecture covers the generation of multivariate Gaussian distributions, including the generation of processes like Wiener process and Brownian bridge. It also discusses the generation of stationary Gaussian random fields and the challenges of factorizing covariance matrices. The instructor explores different algorithms for generating Gaussian distributions and emphasizes the importance of understanding the characteristics of Gaussian variables.
Instructor
duis minim
Ad ex duis eiusmod cillum tempor adipisicing. Aliquip non fugiat mollit nostrud cillum velit aliqua nostrud. Mollit veniam esse labore est magna consectetur et nulla sint occaecat.
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.
Ontological neighbourhood
Related lectures (52)
Quantum Random Number Generation
Explores quantum random number generation, discussing the challenges and implementations of generating good randomness using quantum devices.
Canonical Correlation Analysis: Overview
Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.
Principal Component Analysis: Properties and Applications
Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.
Weyl character formula
Explores the proof of the Weyl character formula for finite-dimensional representations of semisimple Lie algebras.
Power Method
Covers the Power Method for approximating the largest eigenvalue of a matrix.