Explore >> Select a destination
|
You are here 
|
quomodocumque.wordpress.com | ||
| | | | |
www.ethanepperly.com
|
|
| | | | | [AI summary] The provided text is a detailed mathematical exploration of Markov chains, focusing on their convergence properties, the role of reversibility in ensuring convergence to a stationary distribution, and the analysis of total variation distance and chi-squared divergence as measures of convergence. It also includes derivations of bounds on the mixing time of Markov chains and the application of spectral properties of the transition matrix to analyze convergence rates. | |
| | | | |
djalil.chafai.net
|
|
| | | | | This post is devoted to few convex and compact sets of matrices that I like. The set \( {\mathcal{C}_n} \) of correlation matrices. A real \( {n\times n} \) matrix \( {C} \) is a correlation matrix when \( {C} \) is symmetric, semidefinite positive, with unit diagonal. This means that \[ C_{ii}=1, \quad C_{ji}=C_{ji},\quad \left\geq0 \] for every \(... | |
| | | | |
qchu.wordpress.com
|
|
| | | | | In this post we'll describe the representation theory of theadditive group scheme$latex \mathbb{G}_a$ over a field $latex k$. The answer turns out to depend dramatically on whether or not $latex k$ has characteristic zero. Preliminaries over an arbitrary ring (All rings and algebras are commutative unless otherwise stated.) The additive group scheme $latex \mathbb{G}_a$ over... | |
| | | | |
michael-lewis.com
|
|
| | | This is a short summary of some of the terminology used in machine learning, with an emphasis on neural networks. I've put it together primarily to help my own understanding, phrasing it largely in non-mathematical terms. As such it may be of use to others who come from more of a programming than a mathematical background. | ||
