Stochastic modeling
MIT 8.591J Systems Biology, Fall 2014 View the complete course: http://ocw.mit.edu/8-591JF14 Instructor: Jeff Gore Prof. Jeff Gore discusses modeling ...
MIT OpenCourseWare
Mod-05 Lec-22 Numerical Integration
Mathematical Methods in Engineering and Science by Dr. Bhaskar Dasgupta,Department of Mechanical Engineering,IIT Kanpur.For more details on NPTEL visit ...
nptelhrd
JuliaCon 2017 | Stochastic Optimization Models on Power Systems | Camila Metello and Joaquim Garcia
Visit http://julialang.org/ to download Julia.
The Julia Programming Language
Zero-sum stochastic differential games - Daniel Hernández Hernández
Zero-sum stochastic differential games without the Isaacs condition - Daniel Hernández Hernández. CIMAT, México. The collaboration between the probabilistic ...
CIMAT
Intro to solving differential equations in Julia
On February 6 (10AM PST/1 PM EST/19:00 CET) Chris Rackauckas gave an introductory tutorial on solving differential equations in Julia. This tutorial targets ...
The Julia Programming Language
Randomness: Crash Course Statistics #17
There are a lot of events in life that we just can't predict, but just because something is random doesn't mean we don't know or can't learn anything about it.
CrashCourse
Ito Integral-I
Probability and Stochastics for finance
2. More Review; The Bernoulli Process
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
Closed Curves and Periodic Curves | Differential Geometry 4
This video is a continuation of my series on Differential Geometry, and is a discussion about closed and periodic curves. In this video, I begin by discussing the ...
Faculty of Khan
LTI systems, Impulse function, and the Convolution Integral
Get the map of control theory: https://www.redbubble.com/shop/ap/55089837 Download eBook on the fundamentals of control theory (in progress): ...
Brian Douglas
SciPy Beginner's Guide for Optimization
Scipy.Optimize.Minimize is demonstrated for solving a nonlinear objective function subject to general inequality and equality constraints. Source code is ...
APMonitor.com
Mod-07 Lec-29 Monte Carlo simulation approach-5
Stochastic Structural Dynamics by Prof. C.S. Manohar ,Department of Civil Engineering, IISC Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in.
nptelhrd
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 2 - Multi-Task & Meta-Learning Basics
Assistant Professor Chelsea Finn, Stanford University http://cs330.stanford.edu/ To get the latest news on Stanford's upcoming professional programs in Artificial ...
stanfordonline
Stochastic Approximation and Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms
Stochastic approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly ...
Microsoft Research
MA 381: Section 6.2: Functions of a Random Variable Example Worked Out at a Whiteboard
My first whiteboard video! An example of how to determine the probability density function of a function of a random variable X. The CDF technique is used.
Rose-Hulman Online
Bo’az Klartag: On Yuansi Chen’s work on the KLS conjecture II
The Kannan-Lovasz-Simonovits (KLS) conjecture is concerned with the isoperimetric problem in high-dimensional convex bodies. The problem asks for the ...
Hausdorff Center for Mathematics
Neural Differential Equations
This won the best paper award at NeurIPS (the biggest AI conference of the year) out of over 4800 other research papers! Neural Ordinary Differential Equations ...
Siraj Raval
Michael Betancourt: Scalable Bayesian Inference with Hamiltonian Monte Carlo
Recording of Michael Betancourt's talk at the London Machine Learning Meetup: ...
London Machine Learning Meetup
Geometric Brownian Motion (GBM): solution, mean, variance, covariance, calibration, and simulation
Step by step derivation of the GBM's solution, mean, variance, covariance, probability density, calibration /parameter estimation, and simulation of the path and ...
quantpie
Pillai "Characteristic Functions and Moments"
Characteristic function and its usefulness in computing mean and variance of a random variable. Once the characteristic function of a random variable has been ...
Probability, Stochastic Processes - Random Videos
Stochastic Processes 6b
The Wiener Process and the response of dynamic systems to noise using State Space Methods.
keith webber
Mathematical Finance: L6 - Doob’s decomposition & discrete stochastic integrals
Mathe Mannheim
Mod-01 Lec-09 Stochastic process
Performance Evaluation of Computer Systems by Prof.Krishna Moorthy Sivalingam, Department of Computer Science and Engineering, IIT Madras. For more ...
nptelhrd
Gradient Boost Machine Learning|How Gradient boost work in Machine Learning
Gradient Boost Machine Learning|How Gradient boost work in Machine Learning #GradientBoost #GradientBoostMachineLearning #UnfoldDataScience Hello, ...
Unfold Data Science
NONPERTURBATIVE AND NUMERICAL APPROACHES TO QUANTUM GRAVITY, STRING THEORY AND HOLOGRAPHY (ONLINE)
PROGRAM ORGANIZERS: David Berenstein (UCSB), Simon Catterall (Syracuse University), Masanori Hanada (University of Surrey), Anosh Joseph (IISER, ...
International Centre for Theoretical Sciences
Part 1: Rendering Games With Millions of Ray Traced Lights
This is a segment from a two-part video available on NVIDIA On-Demand, entitled “Rendering Game With Millions of Ray Traced Lights”. We encourage you to ...
NVIDIA Developer
L16.7 LMS Estimation with Multiple Observations or Unknowns
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
TU Wien Rendering #32 - Bidirectional Path Tracing, Multiple Importance Sampling
With a classical unidirectional path tracer, we'll have some scenes where it is difficult to connect to the light source, and therefore many of our computed samples ...
Two Minute Papers
Large deviations of Markov processes (Part - 1) by Hugo Touchette
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
Lecture 3 | Loss Functions and Optimization
Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model's predictions, and ...
Stanford University School of Engineering
A Random Walk & Monte Carlo Simulation || Python Tutorial || Learn Python Programming
A random walk is a process where each step is chosen randomly. This technique has many applications. In this video we solve a random walk puzzle using ...
Socratica
Gaussian Mixture Models - The Math of Intelligence (Week 7)
We're going to predict customer churn using a clustering technique called the Gaussian Mixture Model! This is a probability distribution that consists of multiple ...
Siraj Raval
Probability Density Functions (3 of 7: Unknowns in the function)
More resources available at www.misterwootube.com.
Eddie Woo
Mod-03 Lec-08 Optimal Control Formulation Using Calculus of Variations
Optimal Control, Guidance and Estimation by Dr. Radhakant Padhi, Department of Aerospace Engineering, IISc Bangalore. For more details on NPTEL visit ...
nptelhrd
Role of Mathematics in Data Science | Maths for Data Science | Mathematics for Data Science
Intellipaat Data Science architect course: https://intellipaat.com/data-science-architect-masters-program-training/ #WhatIsTheRoleOfMathematicsInDataScience ...
Intellipaat
Statistics And Probability | Overview Of Random Variable & Probability Distribution
This video lecture of Statistics And Probability | Overview Of Random Variable & Distribution | Problems & Concepts by GP Sir will help Engineering and Basic ...
Dr.Gajendra Purohit
3. Law of Large Numbers, Convergence
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
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
Simple Linear Regression Analysis
Linear Regression Analysis and Forecasting
The Map of Quantum Physics
This is the Map of Quantum Physics and quantum mechanics covering everything you need to know about this field in one image. Check out this video's sponsor ...
DoS - Domain of Science
Yuri Maximov: Integration in extremely high dimensions
Data Fest Online 2020 Math Optimization Track https://ods.ai/tracks/optimization-df2020 In this talk we discuss how to compute an integral (or find an expected ...
ODS AI Global
Transformations of random variables -- Example 1
Transformations of random variables -- Example 1.
Lawrence Leemis
