Harmonic functions | Fourier analysis | Potential theory
In mathematics, and specifically in potential theory, the Poisson kernel is an integral kernel, used for solving the two-dimensional Laplace equation, given Dirichlet boundary conditions on the unit disk. The kernel can be understood as the derivative of the Green's function for the Laplace equation. It is named for Siméon Poisson. Poisson kernels commonly find applications in control theory and two-dimensional problems in electrostatics.In practice, the definition of Poisson kernels are often extended to n-dimensional problems. (Wikipedia).
Math 139 Fourier Analysis Lecture 22: Poisson summation formula
Poisson summation formula; heat kernel for the circle; relation with heat kernel on the line. Heat kernel on the circle is an approximation of the identity. Poisson kernel on the disc is the periodization of the Poisson kernel on the upper half plane. Digression into analytic number the
From playlist Course 8: Fourier Analysis
Calculation of the sum of r^|n| e^intheta in closed form, which leads to an interesting fishy kernel (the *fish in French* kernel on the unit disk) Includes applications to real and complex analysis. A delight for anyone who likes power series!
From playlist Integrals
Math 139 Fourier Analysis Lecture 20: Steady-state heat equation in the upper half plane
Statement of problem; formal solution using Fourier transform; definition of Poisson kernel on upper half plane; Fourier transform of Poisson kernel; Poisson kernel is an approximation of the identity; convolution with the Poisson kernel yields a solution; mean value property of harmonic f
From playlist Course 8: Fourier Analysis
Short Introduction to the Poisson Distribution
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Short Introduction to the Poisson Distribution
From playlist Statistics
Brent Pym: Holomorphic Poisson structures - lecture 2
The notion of a Poisson manifold originated in mathematical physics, where it is used to describe the equations of motion of classical mechanical systems, but it is nowadays connected with many different parts of mathematics. A key feature of any Poisson manifold is that it carries a cano
From playlist Virtual Conference
Brent Pym: Holomorphic Poisson structures - lecture 3
The notion of a Poisson manifold originated in mathematical physics, where it is used to describe the equations of motion of classical mechanical systems, but it is nowadays connected with many different parts of mathematics. A key feature of any Poisson manifold is that it carries a cano
From playlist Virtual Conference
The Bergman kernel of the polydisk and the ball
I compute the Bergman kernel of the unit polydisk and the unit Euclidean ball. For my previous video on the Bergman kernel see https://www.youtube.com/watch?v=loIC28LNgNM
From playlist Several Complex Variables
Corner Growth Model on the Circle by Eric Cator
PROGRAM FIRST-PASSAGE PERCOLATION AND RELATED MODELS (HYBRID) ORGANIZERS: Riddhipratim Basu (ICTS-TIFR, India), Jack Hanson (City University of New York, US) and Arjun Krishnan (University of Rochester, US) DATE: 11 July 2022 to 29 July 2022 VENUE: Ramanujan Lecture Hall and online This
From playlist First-Passage Percolation and Related Models 2022 Edited
Daniel Hug: Random tessellations in hyperbolic space - first steps
Random tessellations in Euclidean space are a classical topic and highly relevant for many applications. Poisson hyperplane tessellations present a particular model for which mean values and variances for functionals of interest have been studied successfully and a central limit theory has
From playlist Trimester Seminar Series on the Interplay between High-Dimensional Geometry and Probability
Statistics: Intro to the Poisson Distribution and Probabilities on the TI-84
This video defines a Poisson distribution and then shows how to find Poisson distribution probabilities on the TI-84.
From playlist Geometric Probability Distribution
Wilhem Stannat - Fluctuation limits for mean-field interacting nonlinear Hawkes processes
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From playlist Workshop "Workshop on Mathematical Modeling and Statistical Analysis in Neuroscience" - January 31st - February 4th, 2022
Statistics - 5.3 The Poisson Distribution
The Poisson distribution is used when we know a mean number of successes to expect in a given interval. We will learn what values we need to know and how to calculate the results for probabilities of exactly one value or for cumulative values. Power Point: https://bellevueuniversity-my
From playlist Applied Statistics (Entire Course)
Spatial Events-Spatial Statistics
Spatial point patterns are collections of randomly positioned events in space. Examples include trees in a forest, positions of stars, earthquakes, crime locations, animal sightings, etc. Spatial point data analysis, as a statistical exploration of point patterns, aims to answer questions
From playlist Wolfram Technology Conference 2022
Alberto Cattaneo: An introduction to the BV-BFV Formalism
Abstract: The BV-BFV formalism unifies the BV formalism (which deals with the problem of fixing the gauge of field theories on closed manifolds) with the BFV formalism (which yields a cohomological resolution of the reduced phase space of a classical field theory). I will explain how this
From playlist Topology
Odo Diekmann: Boosting and waning : on the dynamics of immune status
Abstract: The aim is to describe the distribution of immune status in an age-structured population on the basis of a within-host sub-model for continuous waning and occasional boosting. Inspired by both Feller's fundamental work and the more recent delay equation formulation of physiologic
From playlist Mathematics in Science & Technology
Spatial Events: Spatial Statistics
Spatial point patterns are collections of randomly positioned events in space. Examples include trees in a forest, positions of stars, earthquakes, crime locations, animal sightings, etc. Spatial point data analysis, as a statistical exploration of point patterns, aims to answer questions
From playlist Wolfram Technology Conference 2021
10: Time Series - Intro to Neural Computation
MIT 9.40 Introduction to Neural Computation, Spring 2018 Instructor: Michale Fee View the complete course: https://ocw.mit.edu/9-40S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP61I4aI5T6OaFfRK2gihjiMm Covers the Poisson Process, spike train variability, convolutio
From playlist MIT 9.40 Introduction to Neural Computation, Spring 2018
Definition of a Poisson distribution and a solved example of the formula. 00:00 What is a Poisson distribution? 02:39 Poisson distribution formula 03:10 Solved example 04:22 Poisson distribution vs. binomial distribution
From playlist Probability Distributions