/explore
|
You are here 
|
darrenjw.wordpress.com | ||
| | | | |
djalil.chafai.net
|
|
| | | | | Markov-Chains-Monte-Carlo (MCMC for short) methods are widely used in practice for the approximate computation of integrals on various types of spaces. More precisely, let \(\mu\) be a probability measure on \(E\), known only up to a multiplicative constant. Let \(K\) be an irreducible Markov kernel on \(E\). Then by using a classical Metropolis-Hastings type construction, one cook up a computable... | |
| | | | |
www.ethanepperly.com
|
|
| | | | | [AI summary] A computational scientist writes a detailed mathematical proof of the fundamental theorem of Markov chains using coupling methods. | |
| | | | |
667-per-cm.net
|
|
| | | | | This post could also be subtitled "Residual deviance isn't the whole story." My favorite book on logistic regression is by Dr Joseph Hilbe, Logistic Regression Models, CRC Press, 2009, Chapman & Hill. It is a solidly frequentist text, but its discussion of models and rich examples make that besides the point. Except in one case.... | |
| | | | |
jmanton.wordpress.com
|
|
| | | If $latex Y$ is a $latex \sigma(X)$-measurable random variable then there exists a Borel-measurable function $latex f \colon \mathbb{R} \rightarrow \mathbb{R}$ such that $latex Y = f(X)$. The standard proof of this fact leaves several questions unanswered. This note explains what goes wrong when attempting a "direct" proof. It also explains how the standard proof... | ||
