Explore >> Select a destination
|
You are here 
|
almostsuremath.com | ||
| | | | |
extremal010101.wordpress.com
|
|
| | | | | Suppose we want to understand under what conditions on $latex B$ we have $latex \begin{aligned} \mathbb{E} B(f(X), g(Y))\leq B(\mathbb{E}f(X), \mathbb{E} g(Y)) \end{aligned}$holds for all test functions, say real valued $latex f,g$, where $latex X, Y$ are some random variables (not necessarily all possible random variables!). If $latex X=Y$, i.e., $latex X$ and $latex Y$ are... | |
| | | | |
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... | |
| | | | |
www.randomservices.org
|
|
| | | | | [AI summary] The text covers various topics in probability and statistics, including continuous distributions, empirical density functions, and data analysis. It discusses the uniform distribution, rejection sampling, and the construction of continuous distributions without probability density functions. The text also includes data analysis exercises involving empirical density functions for body weight, body length, and gender-specific body weight. | |
| | | | |
yasoob.me
|
|
| | | Hey guys! I recently wrote a review paper regarding the use of Machine Learning in Remote Sensing. I thought that some of you might find it interesting and insightful. It is not strictly a Python focused research paper but is interesting nonetheless. Introduction to Machine Learning and its Usage in Remote Sensing 1. Introduction Machines have allowed us to do complex computations in short amounts of time. This has given rise to an entirely different area of research which was not being explored: teachin... | ||
