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
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
Introduction to the paper https://arxiv.org/abs/2002.06707
From playlist Research
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
Prob & Stats - Markov Chains (9 of 38) What is a Regular Matrix?
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is a regular matrix. Next video in the Markov Chains series: http://youtu.be/loBUEME5chQ
From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes
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
Active hydrodynamics by Sriram Ramaswamy
Stochastic Thermodynamics, Active Matter and Driven Systems DATE: 07 August 2017 to 11 August 2017 VENUE: Ramanujan Lecture Hall, ICTS Bangalore. Stochastic Thermodynamics and Active Systems are areas in statistical physics which have recently attracted a lot of attention and many intere
From playlist Stochastic Thermodynamics, Active Matter and Driven Systems - 2017
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
MIT RES.6-012 Introduction to Probability, Spring 2018 View the complete course: https://ocw.mit.edu/RES-6-012S18 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
From playlist MIT RES.6-012 Introduction to Probability, Spring 2018
Stochastic Gradient Descent: where optimization meets machine learning- Rachel Ward
2022 Program for Women and Mathematics: The Mathematics of Machine Learning Topic: Stochastic Gradient Descent: where optimization meets machine learning Speaker: Rachel Ward Affiliation: University of Texas, Austin Date: May 26, 2022 Stochastic Gradient Descent (SGD) is the de facto op
From playlist Mathematics
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
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
Real-Space Stochastic GW Calculations Benchmark on GW100 by Ishita Shitut
DISCUSSION MEETING : APS SATELLITE MEETING AT ICTS ORGANIZERS : Ranjini Bandyopadhyay (RRI, India), Subhro Bhattacharjee (ICTS-TIFR, India), Arindam Ghosh (IISc, India), Shobhana Narasimhan (JNCASR, India) and Sumantra Sarkar (IISc, India) DATE & TIME: 15 March 2022 to 18 March 2022 VEN
From playlist APS Satellite Meeting at ICTS-2022
Benjamin Gess - Fluctuations in non-equilibrium and stochastic PDE
Macroscopic fluctuation theory provides a general framework for far from equilibrium thermodynamics, based on a fundamental formula for large fluctuations around (local) equilibria. This fundamental postulate can be informally justified from the framework of fluctuating hydrodynamics, link
From playlist Research Spotlight
25. Stochastic Gradient Descent
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Instructor: Suvrit Sra View the complete course: https://ocw.mit.edu/18-065S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k Professor Suvrit Sra g
From playlist MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
Jorge Nocedal: "Tutorial on Optimization Methods for Machine Learning, Pt. 3"
Graduate Summer School 2012: Deep Learning, Feature Learning "Tutorial on Optimization Methods for Machine Learning, Pt. 3" Jorge Nocedal, Northwestern University Institute for Pure and Applied Mathematics, UCLA July 18, 2012 For more information: https://www.ipam.ucla.edu/programs/summ
From playlist GSS2012: Deep Learning, Feature Learning
Paolo Guasoni, Lesson I - 18 december 2017
QUANTITATIVE FINANCE SEMINARS @ SNS PROF. PAOLO GUASONI TOPICS IN PORTFOLIO CHOICE
From playlist Quantitative Finance Seminar @ SNS
Relaxing the I.I.D. Assumption: Adaptive Minimax Optimal Sequential Prediction... - Jeffrey Negrea
Seminar on Theoretical Machine Learning Topic: Relaxing the I.I.D. Assumption: Adaptive Minimax Optimal Sequential Prediction with Expert Advice Speaker: Jeffrey Negrea Affiliation: University of Toronto Date: July 14, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Multiscale flows in an highly viscous fluids by Shankar Ghosh
Stochastic Thermodynamics, Active Matter and Driven Systems DATE: 07 August 2017 to 11 August 2017 VENUE: Ramanujan Lecture Hall, ICTS Bangalore. Stochastic Thermodynamics and Active Systems are areas in statistical physics which have recently attracted a lot of attention and many intere
From playlist Stochastic Thermodynamics, Active Matter and Driven Systems - 2017