Predict Stock-Market Behavior using Markov Chains and R
We apply Markov Chains to map and understand stock-market behavior using the R programming language. By using 2 transition matrices instead of one, we ...
Manuel Amunategui
Regression with a Single Regressor (FRM Part 1 – Book 2 – Chapter 7)
AnalystPrep's FRM Part 1 Video Series For FRM Part 1 Study Notes, Practice Questions, and Mock Exams Register an Account at https://analystprep.com/frm/ ...
AnalystPrep
17. Stochastic Processes II
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Choongbum ...
MIT OpenCourseWare
20. Definitions and Inequalities
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Instructor: Gilbert Strang View the complete course: ...
MIT OpenCourseWare
Finite Math: Markov Chain Steady-State Calculation
Finite Math: Markov Chain Steady-State Calculation. In this video we discuss how to find the steady-state probabilities of a simple Markov Chain. We do this ...
Brandon Foltz
Bayesian generative modelling of stock returns
In this presentation I use PyMC3 to go over the basics of stock price and stock returns modelling, and an overview of the early stochastic volatility models (Clark, ...
Simon Ouellette
19. Weak Law of Large Numbers
MIT 6.041 Probabilistic Systems Analysis and Applied Probability, Fall 2010 View the complete course: http://ocw.mit.edu/6-041F10 Instructor: John Tsitsiklis ...
MIT OpenCourseWare
Mod-01 Lec-05 Stable distributions
Physical Applications of Stochastic Processes by Prof. V. Balakrishnan,Department of Physics,IIT Madras.For more details on NPTEL visit http://nptel.ac.in.
nptelhrd
A Beginner's Guide to Monte Carlo Markov Chain MCMC Analysis 2016
presented by Dr. David Kipping (Columbia)
Sagan Summer Workshop
Markov chain model reduction by Claudio Landim
Large deviation theory in statistical physics: Recent advances and future challenges DATE: 14 August 2017 to 13 October 2017 VENUE: Madhava Lecture Hall, ...
International Centre for Theoretical Sciences
L18.2 The Markov Inequality
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 ...
MIT OpenCourseWare
Gaussian Processes for Time Series Forecasting
Speaker: Juan Orduz Event: Second Symposium on Machine Learning and Dynamical Systems http://www.fields.utoronto.ca/activities/20-21/dynamical Title: ...
Fields Institute
Reinforcement Learning 4: Model-Free Prediction and Control
Hado van Hasselt, Research Scientist, discusses model-free prediction and controls as part of the Advanced Deep Learning & Reinforcement Learning ...
DeepMind
17. Bayesian Statistics
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet ...
MIT OpenCourseWare
Intuitive proofs of Ergodic Theorems
Ergodic Theorems are widely used in dynamical systems and Probability Theory. In this expository lecture, I will present simple proofs of the Birkhoff Pointwise ...
Microsoft Research
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 5 - Value Function Approximation
Professor Emma Brunskill, Stanford University http://onlinehub.stanford.edu/ Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for ...
stanfordonline
Gang George Yin: "High-Dimensional HJBs: Mean-Field Limits and McKean-Vlasov Equations"
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop I: High Dimensional Hamilton-Jacobi Methods in Control and Differential Games "High-Dimensional ...
Institute for Pure & Applied Mathematics (IPAM)
Dynamical phase transitions in Markov processes by Hugo Touchette
COLLOQUIUM DYNAMICAL PHASE TRANSITIONS IN MARKOV PROCESSES SPEAKER: Hugo Touchette (Stellenbosch University, South Africa) DATE: Mon, ...
International Centre for Theoretical Sciences
12. Renewal Rewards, Stopping Trials, and Wald's Inequality
MIT 6.262 Discrete Stochastic Processes, Spring 2011 View the complete course: http://ocw.mit.edu/6-262S11 Instructor: Robert Gallager License: Creative ...
MIT OpenCourseWare
Bayesian Data Science: Probabilistic Programming | SciPy 2019 Tutorial | Eric Ma
This tutorial will introduce you to the wonderful world of Bayesian data science through the lens of probabilistic programming. In the first hour of the tutorial, we ...
Enthought
Konstantin Khanin: Between mathematics and physics
Abstract: Over the past few decades we have witnessed an unparalleled process of unification between mathematics and physics. In this talk we shall discuss ...
The Abel Prize
Simulation Methodology: An Overview (Part 2)
Peter Glynn (Stanford) Simulation Methodology: An Overview (Part 2) Theory of Reinforcement Learning Boot Camp.
Simons Institute
15. Poisson Process II
MIT 6.041 Probabilistic Systems Analysis and Applied Probability, Fall 2010 View the complete course: http://ocw.mit.edu/6-041F10 Instructor: John Tsitsiklis ...
MIT OpenCourseWare
Frontiers in Machine Learning: Big Ideas in Causality and Machine Learning
Causal relationships are stable across distribution shifts. Models based on causal knowledge have the potential to generalize to unseen domains and offer ...
Microsoft Research
Deep Generative models and Inverse Problems - Alexandros Dimakis
Seminar on Theoretical Machine Learning Topic:Deep Generative models and Inverse Problems Speaker: Alexandros Dimakis Affiliation: University of Texas at ...
Institute for Advanced Study
Pedro Domingos - Unifying Logical and Statistical AI
Unifying Logical and Statistical AI Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the ...
The University of Edinburgh
Mod-01 Lec-10 Birth-and-death processes
Physical Applications of Stochastic Processes by Prof. V. Balakrishnan,Department of Physics,IIT Madras.For more details on NPTEL visit http://nptel.ac.in.
nptelhrd
The History of Mathematics and Its Applications
STEMerch Store: https://stemerch.com/ Support the Channel: https://www.patreon.com/zachstar PayPal(one time donation): https://www.paypal.me/ZachStarYT ...
Zach Star
S09.1 Buffon's Needle & Monte Carlo Simulation
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 ...
MIT OpenCourseWare
Law of large numbers | Probability and Statistics | Khan Academy
Introduction to the law of large numbers Watch the next lesson: ...
Khan Academy
Rotations of the circle and renormalization 5
Speaker: Corinna Ulcigrai Summer School in Dynamics (Introductory and Advanced) | (smr 3226) 2018_07_20-10_20-smr3226.
ICTP Mathematics
Diffusive limits for random walks and diffusions with long memory – B. Tóth – ICM2018
Probability and Statistics Invited Lecture 12.3 Diffusive and super-diffusive limits for random walks and diffusions with long memory Bálint Tóth Abstract: We ...
Rio ICM2018
Pacific Northwest Probability Seminar: A Characterization Theorem for the Gaussian Free Field
We prove that any random distribution satisfying conformal invariance and a form of domain Markov property and having a finite moment condition must be the ...
Microsoft Research
Stochastics and Statistics Seminar - Fall 2020 - Gesine Reinert, University of Oxford
Stein's method for multivariate continuous distributions and applications.
MIT Institute for Data, Systems, and Society
Lecture 19: Joint, Conditional, and Marginal Distributions | Statistics 110
We discuss joint, conditional, and marginal distributions (continuing from Lecture 18), the 2-D LOTUS, the fact that E(XY)=E(X)E(Y) if X and Y are independent, ...
Harvard University
Convergence in Probability and in the Mean Part 1
MIT 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013 View the complete course: http://ocw.mit.edu/6-041SCF13 Instructor: Kuang Xu ...
MIT OpenCourseWare
Joint ICTP-SISSA Colloquium by Prof. David Wolpert on "The Stochastic Thermodynamics of Computation"
Prof. David Wolpert, Santa Fe Institute, USA Abstract: One of the central concerns of computer science is how the resources needed to perform a given ...
Int'l Centre for Theoretical Physics
Christopher Vairogs: Quantum state discrimination circuits inspired by Deutschian CTCs
Abstract: The Holevo-Helstrom theorem places a bound on the probability of perfectly distinguishing two non-orthogonal states in a single measurement.
LaQuTeC
Eric Ma, Hugo Bowne-Anderson - Bayesian Data Science by Simulation - PyCon 2019
"Speakers: Eric Ma, Hugo Bowne-Anderson This tutorial is an Introduction to Bayesian data science through the lens of simulation or hacker statistics. We will ...
PyCon 2019
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 ...
Rio ICM2018
Lecture75 (Data2Decision) Bayesian Regression, part 2
Summarizing the posterior distribution, uninformative prior, substantive prior, Bernstein-von Mises theorem, credible intervals. Course Website: ...
Chris Mack
Connections between physics and deep learning
Max Tegmark - MIT.
MITCBMM
