Integral geometry | Stochastic processes | Spatial processes
In mathematics, stochastic geometry is the study of random spatial patterns. At the heart of the subject lies the study of random point patterns. This leads to the theory of spatial point processes, hence notions of Palm conditioning, which extend to the more abstract setting of random measures. (Wikipedia).
Introduction to the paper https://arxiv.org/abs/2002.06707
From playlist Research
Francois Baccelli: High dimensional stochastic geometry in the Shannon regime
This talk will focus on Euclidean stochastic geometry in the Shannon regime. In this regime, the dimension n of the Euclidean space tends to infinity, point processes have intensities which are exponential functions of n, and the random compact of interest sets have diameters of order squa
From playlist Workshop: High dimensional spatial random systems
Basic stochastic simulation b: Stochastic simulation algorithm
(C) 2012-2013 David Liao (lookatphysics.com) CC-BY-SA Specify system Determine duration until next event Exponentially distributed waiting times Determine what kind of reaction next event will be For more information, please search the internet for "stochastic simulation algorithm" or "kin
From playlist Probability, statistics, and stochastic processes
Dr Lukasz Szpruch, University of Edinburgh
Bio I am a Lecturer at the School of Mathematics, University of Edinburgh. Before moving to Scotland I was a Nomura Junior Research Fellow at the Institute of Mathematics, University of Oxford, and a member of Oxford-Man Institute for Quantitative Finance. I hold a Ph.D. in mathematics fr
From playlist Short Talks
Jana Cslovjecsek: Efficient algorithms for multistage stochastic integer programming using proximity
We consider the problem of solving integer programs of the form min {c^T x : Ax = b; x geq 0}, where A is a multistage stochastic matrix. We give an algorithm that solves this problem in fixed-parameter time f(d; ||A||_infty) n log^O(2d) n, where f is a computable function, d is the treed
From playlist Workshop: Parametrized complexity and discrete optimization
21. Stochastic Differential Equations
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Choongbum Lee This lecture covers the topic of stochastic differential equations, linking probability theory with ordinary and partial differential
From playlist MIT 18.S096 Topics in Mathematics w Applications in Finance
Felix Otto: Singular SPDE with rough coefficients
Abstract: We are interested in parabolic differential equations (∂t−a∂2x)u=f with a very irregular forcing f and only mildly regular coefficients a. This is motivated by stochastic differential equations, where f is random, and quasilinear equations, where a is a (nonlinear) function of u.
From playlist Probability and Statistics
DDPS | Data-driven information geometry approach to stochastic model reduction
Description: Reduced-order models are often obtained by projection onto a subspace; standard least squares in linear spaces is a familiar technique that can also be applied to stochastic phenomena as exemplified by polynomial chaos expansions. Optimal approximants are obtained by minimizin
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Inflation, geometry and stochasticity - S. Renaux-Petel - Workshop 1 - CEB T3 2018
Sebastien Renaux-Petel (IAP) / 18.09.2018 Inflation, geometry and stochasticity ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter : https://twitter.com/InHe
From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology
Panorama of Mathematics: Felix Otto
Panorama of Mathematics To celebrate the tenth year of successful progression of our cluster of excellence we organized the conference "Panorama of Mathematics" from October 21-23, 2015. It outlined new trends, results, and challenges in mathematical sciences. Felix Otto: "A large-scale
From playlist Panorama of Mathematics
Prob & Stats - Markov Chains (8 of 38) What is a Stochastic Matrix?
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is a stochastic matrix. Next video in the Markov Chains series: http://youtu.be/YMUwWV1IGdk
From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes
GPDE Workshop - External doubly stochastic measures and optimal transportation
Robert McCann University of Toronto February 23, 2009 For more videos, visit http://video.ias.edu
From playlist Mathematics
Ngoc Mai Tran: Stochastic geometry to generalize the Mondrian process
The Mondrian process is a stochastic process that produces a recursive partition of space with random axis-aligned cuts. Random forests and Laplace kernel approximations built from the Mondrian process have led to efficient online learning methods and Bayesian optimization. By viewing the
From playlist Workshop: High dimensional spatial random systems
Dynamical, symplectic and stochastic perspectives on optimization – Michael Jordan – ICM2018
Plenary Lecture 20 Dynamical, symplectic and stochastic perspectives on gradient-based optimization Michael Jordan Abstract: Our topic is the relationship between dynamical systems and optimization. This is a venerable, vast area in mathematics, counting among its many historical threads
From playlist Plenary Lectures
Alexander Schmeding: A geometric view on stochastic Euler equations
The lecture was held within the of the Hausdorff Junior Trimester Program: Randomness, PDEs and Nonlinear Fluctuations. Abstract: We consider a stochastic version of Euler equations. Due to a trick devised by V. Arnold in the deterministic setting, one can rewrite certain stochastic PDEs
From playlist HIM Lectures: Junior Trimester Program "Randomness, PDEs and Nonlinear Fluctuations"
From playlist Contributed talks One World Symposium 2020
Peter Pivovarov: Random s-concave functions and isoperimetry
I will discuss stochastic geometry of s-concave functions. In particular, I will explain how a ”local” stochastic isoperimetry underlies several functional inequalities. A new ingredient is a notion of shadow systems for s-concave functions. Based on joint works with J. Rebollo Bueno.
From playlist Workshop: High dimensional spatial random systems
"Data-Driven Optimization in Pricing and Revenue Management" by Arnoud den Boer - Lecture 1
In this course we will study data-driven decision problems: optimization problems for which the relation between decision and outcome is unknown upfront, and thus has to be learned on-the-fly from accumulating data. This type of problems has an intrinsic tension between statistical goals a
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management
