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YouTube. IHÉS. 18 May 2017. "Planar Ising model at criticality: State-of-the-art and perspectives — Dmitry Chelkak". YouTube. Rio ICM2018. 2 October 2018. Dmitry Chelkak - Planar Ising model: from combinatorics to CFT and s-embeddings, Lectures 1–4, U. of Virginia Integrable Probability Summer School "Lecture 1". YouTube. 29 May 2019 ...
The platform hosted the Messenger Lectures series titled The Character of Physical Law given at Cornell University by Richard Feynman in 1964 and recorded by the BBC. [1] According to his video introduction, Gates saw the lectures when he was younger. [ 2 ]
Roland Speicher (born 12 June 1960) is a German mathematician, known for his work on free probability theory.He is a professor at the Saarland University.After winning the 1979 German national competition Jugend forscht in the field of mathematics and computer science, [1] Speicher studied physics and mathematics at the Universities of Saarbrücken, Freiburg and Heidelberg.
Leonard Susskind (/ ˈ s ʌ s k ɪ n d /; born June 16, 1940) [2] [3] is an American theoretical physicist, Professor of theoretical physics at Stanford University and founding director of the Stanford Institute for Theoretical Physics.
Free probability is a mathematical theory that studies non-commutative random variables. The "freeness" or free independence property is the analogue of the classical notion of independence , and it is connected with free products .
This theorem follows from the fact that if X n converges in distribution to X and Y n converges in probability to a constant c, then the joint vector (X n, Y n) converges in distribution to (X, c) . Next we apply the continuous mapping theorem , recognizing the functions g ( x , y ) = x + y , g ( x , y ) = xy , and g ( x , y ) = x y −1 are ...
If X n converges in probability to X, and if P(| X n | ≤ b) = 1 for all n and some b, then X n converges in rth mean to X for all r ≥ 1. In other words, if X n converges in probability to X and all random variables X n are almost surely bounded above and below, then X n converges to X also in any rth mean. [10] Almost sure representation ...
In probability theory and statistics, the characteristic function of any real-valued random variable completely defines its probability distribution. If a random variable admits a probability density function , then the characteristic function is the Fourier transform (with sign reversal) of the probability density function.