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
Understanding Discrete Event Simulation, Part 3: Leveraging Stochastic Processes
Watch more MATLAB Tech Talks: https://goo.gl/ktpVB7 Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn how discrete-event simulation uses stochastic processes, in which aspects of a system are randomized, in this MATLAB®
From playlist Understanding Discrete-Event Simulation - MATLAB Tech Talks
Applied Math Perspectives on Stochastic Climate Models ( 2 ) - Andrew J. Majda
Lecture 2: Applied Math Perspectives on Stochastic Climate Models Abstract: We are entering a new era of Stochastic Climate Modeling. Such an approach is needed for several reasons: 1) to model crucial poorly represented processes in contemporary comprehensive computer models such as inte
From playlist Mathematical Perspectives on Clouds, Climate, and Tropical Meteorology
Introduction to the paper https://arxiv.org/abs/2002.06707
From playlist Research
"Diffusion Approximation and Sequential Experimentation" by Victor Araman
We consider a Bayesian sequential experimentation problem. We identify environments in which the average number of experiments that is conducted per unit of time is large and the informativeness of each individual experiment is low. Under such regimes, we derive a diffusion approximation f
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management
Iterative stochastic numerical methods for statistical sampling: Professor Ben Leimkuhler
I study the design, analysis and implementation of algorithms for time-dependent phenomena and modelling for problems in engineering and the sciences. My previous works have helped to establish the foundations of molecular simulation, providing efficient deterministic and stochastic numeri
From playlist Data science classes
An introduction to multilevel Monte Carlo methods – Michael Giles – ICM2018
Numerical Analysis and Scientific Computing Invited Lecture 15.7 An introduction to multilevel Monte Carlo methods Michael Giles Abstract: In recent years there has been very substantial growth in stochastic modelling in many application areas, and this has led to much greater use of Mon
From playlist Numerical Analysis and Scientific Computing
Tony Lelievre (DDMCS@Turing): Coarse-graining stochastic dynamics
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems
Discrete stochastic simulation of spatially inhomogeneous biochemical systems
Linda Petzold (University of California, Santa Barbara). Plenary Lecture from the 1st PRIMA Congress, 2009. Plenary Lecture 6. Abstract: In microscopic systems formed by living cells, the small numbers of some reactant molecules can result in dynamical behavior that is discrete and stocha
From playlist PRIMA2009
Markov processes and applications-3 by Hugo Touchette
PROGRAM : BANGALORE SCHOOL ON STATISTICAL PHYSICS - XII (ONLINE) ORGANIZERS : Abhishek Dhar (ICTS-TIFR, Bengaluru) and Sanjib Sabhapandit (RRI, Bengaluru) DATE : 28 June 2021 to 09 July 2021 VENUE : Online Due to the ongoing COVID-19 pandemic, the school will be conducted through online
From playlist Bangalore School on Statistical Physics - XII (ONLINE) 2021
Stochastic modelling of geophysical flows - Mémin - Workshop 2 - CEB T3 2019
Mémin (INRIA, FR) / 13.11.2019 Stochastic modelling of geophysical flows ---------------------------------- 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 2019 - T3 - The Mathematics of Climate and the Environment
Hybrid Deterministic-Stochastic Modeling
Robert Nachbar explains how Mathematica and C were used to develop a hybrid deterministic-stochastic simulation engine based on differential equations and the chemical master equation. He highlights some interesting aspects of the implementation and demonstrates its use in this talk from t
From playlist Wolfram Technology Conference 2012
AQC 2016 - Adiabatic Quantum Computer vs. Diffusion Monte Carlo
A Google TechTalk, June 29, 2016, presented by Stephen Jordan (NIST) ABSTRACT: While adiabatic quantum computation using general Hamiltonians has been proven to be universal for quantum computation, the vast majority of research so far, both experimental and theoretical, focuses on stoquas
From playlist Adiabatic Quantum Computing Conference 2016
Markov processes and applications-4 by Hugo Touchette
PROGRAM : BANGALORE SCHOOL ON STATISTICAL PHYSICS - XII (ONLINE) ORGANIZERS : Abhishek Dhar (ICTS-TIFR, Bengaluru) and Sanjib Sabhapandit (RRI, Bengaluru) DATE : 28 June 2021 to 09 July 2021 VENUE : Online Due to the ongoing COVID-19 pandemic, the school will be conducted through online
From playlist Bangalore School on Statistical Physics - XII (ONLINE) 2021
Part1. Data assimilation using particle filters... - Crisan - Workshop 2 - CEB T3 2019
Crisan (Imperial College London, UK) / 13.11.2019 Data assimilation using particle filters for class of partially observed stochastic geophysical fluid dynamics models. Part I ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actua
From playlist 2019 - T3 - The Mathematics of Climate and the Environment
AQC 2016 - Testing Adiabatic Quantum Computers Using Simple Quantum Simulation
A Google TechTalk, June 29, 2016, presented by Peter Love (Tufts University) ABSTRACT: Validation of Adiabatic Quantum computers is a significant problem. One of the advantages of the Adiabatic model is that it does not require the rapid pulsed controls necessary in the gate model of quan
From playlist Adiabatic Quantum Computing Conference 2016
"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