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
Automatizability and Simple Stochastic Games - Toniann Pitassi
Automatizability and Simple Stochastic Games Toniann Pitassi University of Toronto February 15, 2011 The complexity of simple stochastic games (SSGs) has been open since they were defined by Condon in 1992. Such a game is played by two players, Min and Max, on a graph consisting of max nod
From playlist Mathematics
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
IDTIMWYTIM: Stochasticity - THAT'S Random
Hank helps us understand the difference between the colloquial meaning of randomness, and the scientific meaning, which is also known as stochasticity. We will learn how, in fact, randomness is surprisingly predictable. Like SciShow: http://www.facebook.com/scishow Follow SciShow: http://
From playlist Uploads
The Mathematics of Trust // How Game Theory Explains Cooperation
Check out Brilliant ► https://brilliant.org/TreforBazett/ Join for free and the first 200 subscribers get 20% off an annual premium subscription. Thank you to Brilliant for sponsoring this playlist on Game Theory. My Game Theory Playlist ► https://www.youtube.com/playlist?list=PLHXZ9OQG
From playlist Game Theory
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
Brain Teasers: 10. Winning in a Markov chain
In this exercise we use the absorbing equations for Markov Chains, to solve a simple game between two players. The Zoom connection was not very stable, hence there are a few audio problems. Sorry.
From playlist Brain Teasers and Quant Interviews
Game Playing 1 - Minimax, Alpha-beta Pruning | Stanford CS221: AI (Autumn 2019)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Cke8v4 Topics: Minimax, expectimax, Evaluation functions, Alpha-beta pruning Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford Univer
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021
Jules Hedges - compositional game theory - part IV
Compositional game theory is an approach to game theory that is designed to have better mathematical (loosely “algebraic” and “geometric”) properties, while also being intended as a practical setting for microeconomic modelling. It gives a graphical representation of games in which the flo
From playlist compositional game theory
Hugo Gimbert: Two-player perfect-information shift-invariant submixing stochastic games are [...]
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 Mathematical Aspects of Computer Science
Mean-Field Games Stochastic Differential Mean-Field Games (Lecture 3) by Kavita Ramanan
PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear
From playlist Advances in Applied Probability 2019
Chenchen Mou: "Weak solutions of second order master equations for MFGs with common noise"
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop III: Mean Field Games and Applications "Weak solutions of second order master equations for mean field games with common noise" Chenchen Mou - University of California, Los Angeles (UCLA) Abstract: In this talk we study master equations
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
Tamer Başar: "A General Theory for Discrete-Time Mean-Field Games"
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop III: Mean Field Games and Applications "A General Theory for Discrete-Time Mean-Field Games" Tamer Başar - University of Illinois at Urbana-Champaign Abstract: In this lecture, I will present a general theory for mean-field games formul
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
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
Randomness Quiz - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
In this presentation from the Wolfram Technology Conference, Gerald Thomas explores some applications of decision process theory, which uses differential geometry techniques to predict future decisions. For more information about Mathematica, please visit: http://www.wolfram.com/mathemat
From playlist Wolfram Technology Conference 2012
MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL)
First lecture of MIT course 6.S091: Deep Reinforcement Learning, introducing the fascinating field of Deep RL. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code t
From playlist Introduction to Deep Learning
Lecture Lorenzo Pareschi: Uncertainty quantification for kinetic equations III
The lecture was held within the of the Hausdorff Trimester Program: Kinetic Theory Abstract: In these lectures we overview some recent results in the field of uncertainty quantification for kinetic equations with random inputs. Uncertainties may be due to various reasons, like lack of kn
From playlist Summer School: Trails in kinetic theory: foundational aspects and numerical methods
Introduction to the paper https://arxiv.org/abs/2002.06707
From playlist Research
Maxime Laborde: "A Lyapunov analysis for accelerated gradient methods: From deterministic to sto..."
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop II: PDE and Inverse Problem Methods in Machine Learning "A Lyapunov analysis for accelerated gradient methods: From deterministic to stochastic case" Maxime Laborde - McGill University Abstract: Su, Boyd and Candés showed that Nesterov'
From playlist High Dimensional Hamilton-Jacobi PDEs 2020