In probability theory, concentration inequalities provide bounds on how a random variable deviates from some value (typically, its expected value). The law of large numbers of classical probability theory states that sums of independent random variables are, under very mild conditions, close to their expectation with a large probability. Such sums are the most basic examples of random variables concentrated around their mean. Recent results show that such behavior is shared by other functions of independent random variables. Concentration inequalities can be sorted according to how much information about the random variable is needed in order to use them. (Wikipedia).
Radek Adamczak: Functional inequalities and concentration of measure I
Concentration inequalities are one of the basic tools of probability and asymptotic geo- metric analysis, underlying the proofs of limit theorems and existential results in high dimensions. Original arguments leading to concentration estimates were based on isoperimetric inequalities, whic
From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability
Radek Adamczak: Functional inequalities and concentration of measure II
Concentration inequalities are one of the basic tools of probability and asymptotic geo- metric analysis, underlying the proofs of limit theorems and existential results in high dimensions. Original arguments leading to concentration estimates were based on isoperimetric inequalities, whic
From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability
Radek Adamczak: Functional inequalities and concentration of measure III
Concentration inequalities are one of the basic tools of probability and asymptotic geo- metric analysis, underlying the proofs of limit theorems and existential results in high dimensions. Original arguments leading to concentration estimates were based on isoperimetric inequalities, whic
From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability
Jean-René Chazottes: A brief introduction to concentration inequalities
$Let (X,T)$ be a dynamical system preserving a probability measure $\mu $. A concentration inequality quantifies how small is the probability for $F(x,Tx,\ldots,T^{n-1}x)$ to deviate from $\int F(x,Tx,\ldots,T^{n-1}x) \mathrm{d}\mu(x)$ by an given amount $u$, where $F:X^n\to\mathbb{R}$ is
From playlist Probability and Statistics
Solutions to Basic OR Compound Inequalities
This video explains how to graph the solution to an OR compound inequality and express the solution using interval notation. http://mathispower4u.com
From playlist Solving and Graphing Compound Inequalities
Why do we have to flip the sign when we divide or multiply by negative one - Cool Math
👉 Learn about solving an inequality and graphing it's solution. An inequality is a relation where the expression in the left hand side is not equal to the expression in the right hand side of the inequality sign. A linear inequality is an inequality whose highest power in the variable(s) i
From playlist Solve and Graph Inequalities | Learn About
Solving and Graphing an inequality when the solution point is a decimal
👉 Learn how to solve multi-step linear inequalities having parenthesis. An inequality is a statement in which one value is not equal to the other value. An inequality is linear when the highest exponent in its variable(s) is 1. (i.e. there is no exponent in its variable(s)). A multi-step l
From playlist Solve and Graph Inequalities | Multi-Step With Parenthesis
Summary for solving one variable inequalities
👉 Learn about solving an inequality and graphing it's solution. An inequality is a relation where the expression in the left hand side is not equal to the expression in the right hand side of the inequality sign. A linear inequality is an inequality whose highest power in the variable(s) i
From playlist Solve and Graph Inequalities | Learn About
Concentration of quantum states from quantum functional (...) - N. Datta - Workshop 2 - CEB T3 2017
Nilanjana Datta / 24.10.17 Concentration of quantum states from quantum functional and transportation cost inequalities Quantum functional inequalities (e.g. the logarithmic Sobolev- and Poincaré inequalities) have found widespread application in the study of the behavior of primitive q
From playlist 2017 - T3 - Analysis in Quantum Information Theory - CEB Trimester
Summary for solving and graphing compound inequalities
👉 Learn all about solving and graphing compound inequalities. An inequality is a statement in which one value is not equal to the other value. A compound inequality is a type of inequality comprising of more than one inequalities. To solve a compound inequality, we use inverse operations
From playlist Solve Compound Inequalities
Bruno Torresani: Continuous and discrete uncertainty principles
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist 30 years of wavelets
Sébastien Gouëzel: Concentration properties of dynamical systems
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Probability and Statistics
Franca Hoffmann: Reversed Hardy Littlewood Sobolev Inequalities
The lecture was held within the framework of the Hausdorff Trimester Program: Kinetic Theory Abstract: We will introduce a new family of reverse Hardy-Littlewood-Sobolev inequalities which involve a power law kernel with positive exponent. We investigate the range of the admissible param
From playlist Workshop: Probabilistic and variational methods in kinetic theory
Joe Neeman: Gaussian isoperimetry and related topics I
The Gaussian isoperimetric inequality gives a sharp lower bound on the Gaussian surface area of any set in terms of its Gaussian measure. Its dimension-independent nature makes it a powerful tool for proving concentration inequalities in high dimensions. We will explore several consequence
From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability
Nexus trimester - Ayfer Özgür (Stanford)
Improving on the cutset bound via a measure concentration Ayfer Özgür (Stanford) March 01, 2016 Abstract: The cut-set bound developed by Cover and El Gamal in 1979 has since remained the best known upper bound on the capacity of the Gaussian relay channel. We develop a new upper bound on
From playlist Nexus Trimester - 2016 - Central Workshop
Sharp matrix concentration inequalities - Ramon van Handel
Computer Science/Discrete Mathematics Seminar I Topic: Sharp matrix concentration inequalities Speaker: Ramon van Handel Affiliation: Princeton University Date: October 18, 2021 What does the spectrum of a random matrix look like when we make no assumption whatsoever about the covarianc
From playlist Mathematics
What are compound inequalities
👉 Learn all about solving and graphing compound inequalities. An inequality is a statement in which one value is not equal to the other value. A compound inequality is a type of inequality comprising of more than one inequalities. To solve a compound inequality, we use inverse operations
From playlist Solve Compound Inequalities