Lévy processes | Martingale theory | Wiener process
In mathematics, the Wiener process is a real-valued continuous-time stochastic process named in honor of American mathematician Norbert Wiener for his investigations on the mathematical properties of the one-dimensional Brownian motion. It is often also called Brownian motion due to its historical connection with the physical process of the same name originally observed by Scottish botanist Robert Brown. It is one of the best known Lévy processes (càdlàg stochastic processes with stationary independent increments) and occurs frequently in pure and applied mathematics, economics, quantitative finance, evolutionary biology, and physics. The Wiener process plays an important role in both pure and applied mathematics. In pure mathematics, the Wiener process gave rise to the study of continuous time martingales. It is a key process in terms of which more complicated stochastic processes can be described. As such, it plays a vital role in stochastic calculus, diffusion processes and even potential theory. It is the driving process of Schramm–Loewner evolution. In applied mathematics, the Wiener process is used to represent the integral of a white noise Gaussian process, and so is useful as a model of noise in electronics engineering (see Brownian noise), instrument errors in filtering theory and disturbances in control theory. The Wiener process has applications throughout the mathematical sciences. In physics it is used to study Brownian motion, the diffusion of minute particles suspended in fluid, and other types of diffusion via the Fokker–Planck and Langevin equations. It also forms the basis for the rigorous path integral formulation of quantum mechanics (by the Feynman–Kac formula, a solution to the Schrödinger equation can be represented in terms of the Wiener process) and the study of eternal inflation in physical cosmology. It is also prominent in the mathematical theory of finance, in particular the Black–Scholes option pricing model. (Wikipedia).
Heap Sort - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Large deviations for the Wiener Sausage by Frank den Hollander
Large deviation theory in statistical physics: Recent advances and future challenges DATE: 14 August 2017 to 13 October 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru Large deviation theory made its way into statistical physics as a mathematical framework for studying equilibrium syst
From playlist Large deviation theory in statistical physics: Recent advances and future challenges
Large deviations for the Wiener Sausage (Lecture 2) by Frank den Hollander
Large deviation theory in statistical physics: Recent advances and future challenges DATE: 14 August 2017 to 13 October 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru Large deviation theory made its way into statistical physics as a mathematical framework for studying equilibrium syst
From playlist Large deviation theory in statistical physics: Recent advances and future challenges
Solve a System of Linear Equations Using Elimination
👉Learn how to solve a system (of equations) by elimination. A system of equations is a set of equations which are collectively satisfied by one solution of the variables. The elimination method of solving a system of equations involves making the coefficient of one of the variables to be e
From playlist Solve a System of Equations Using Elimination | Medium
Solve a System of Linear Equations Using Elimination
👉Learn how to solve a system (of equations) by elimination. A system of equations is a set of equations which are collectively satisfied by one solution of the variables. The elimination method of solving a system of equations involves making the coefficient of one of the variables to be e
From playlist Solve a System of Equations Using Elimination | Medium
Solve a System of Linear Equations Using Elimination
👉Learn how to solve a system (of equations) by elimination. A system of equations is a set of equations which are collectively satisfied by one solution of the variables. The elimination method of solving a system of equations involves making the coefficient of one of the variables to be e
From playlist Solve a System of Equations Using Elimination | Medium
Solve a System of Linear Equations Using Elimination
👉Learn how to solve a system (of equations) by elimination. A system of equations is a set of equations which are collectively satisfied by one solution of the variables. The elimination method of solving a system of equations involves making the coefficient of one of the variables to be e
From playlist Solve a System of Equations Using Elimination | Medium
Solve a system of equation when they are the same line
👉Learn how to solve a system (of equations) by elimination. A system of equations is a set of equations which are collectively satisfied by one solution of the variables. The elimination method of solving a system of equations involves making the coefficient of one of the variables to be e
From playlist Solve a System of Equations Using Elimination | Medium
Pork and meat products made in a United States factory sometime in the 1950's. A feature of the U.S. pig and hog industry has been the rapid shift to fewer and larger operations, associated with the advent of electricity, and technological change created an ever evolving structure.
From playlist Mechanical Engineering
Financial Option Theory with Mathematica -- Basics of SDEs and Option Pricing
This is my first session of my Financial Option Theory with Mathematica track. I provide an introduction to financial options, develop the relevant SDEs (stochastic differential equations), and then apply them to stock price processes and the pricing of (European) options. You can downloa
From playlist Financial Options Theory with Mathematica
On the numerical integration of the Lorenz-96 model... - Grudzien - Workshop 2 - CEB T3 2019
Grudzien (U Nevada in Reno, USA) / 13.11.2019 On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook
From playlist 2019 - T3 - The Mathematics of Climate and the Environment
Yair Shenfeld - The Brownian transport map - IPAM at UCLA
Recorded 09 February 2022. Yair Shenfeld of the Massachusetts Institute of Technology presents "The Brownian transport map" at IPAM's Calculus of Variations in Probability and Geometry Workshop. Abstract: The existence of Lipschitz transport maps between probability measures leads to tran
From playlist Workshop: Calculus of Variations in Probability and Geometry
Lec 16 | MIT 18.085 Computational Science and Engineering I
Dynamic estimation: Kalman filter and square root filter A more recent version of this course is available at: http://ocw.mit.edu/18-085f08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 18.085 Computational Science & Engineering I, Fall 2007
Financial Option Theory with Mathematica -- Volatility, and direct solution of PDEs
This is my third session of my track about Financial Option Theory with Mathematica. I first develop two methods to compute historical volatility of a stock. Next I do the same for an estimate of the historical appreciation rate. I then come to the very important topic of the implied volat
From playlist Financial Options Theory with Mathematica
Special Topics - The Kalman Filter (1 of 55) What is a Kalman Filter?
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is Kalman filter and how is it used. Next video in this series can be seen at: https://youtu.be/tk3OJjKTDnQ
From playlist SPECIAL TOPICS 1 - THE KALMAN FILTER
Why Use Kalman Filters? | Understanding Kalman Filters, Part 1
Download our Kalman Filter Virtual Lab to practice linear and extended Kalman filter design of a pendulum system with interactive exercises and animations in MATLAB and Simulink: https://bit.ly/3g5AwyS Discover common uses of Kalman filters by walking through some examples. A Kalman filte
From playlist Understanding Kalman Filters
Playing with Today's New Toy: Wolfram Language Version 11.2
Stephen takes Version 11.2 of the Wolfram Language for a spin, showing off its new functionalities and capabilities. For upcoming live streams by Stephen Wolfram, please visit: http://www.stephenwolfram.com/livestreams/
From playlist Stephen Wolfram Livestreams
Solving a system of equations with infinite many solutions
👉Learn how to solve a system (of equations) by elimination. A system of equations is a set of equations which are collectively satisfied by one solution of the variables. The elimination method of solving a system of equations involves making the coefficient of one of the variables to be e
From playlist Solve a System of Equations Using Elimination | Medium
Gram Schmidt process for QR decomposition using Julia
In this video tutorial I use the Julia Language to explain the easy steps of the Gram Schmidt process. Following the steps of this process yields a set of orthonormal basis vectors for the (sub) space spanned by the column vectors of a matrix. Each new column vector is orthogonal to all th
From playlist Julia on Coursera
La théorie l’information sans peine - Bourbaphy - 17/11/18
Olivier Rioul (Telecom Paris Tech) / 17.11.2018 La théorie l’information sans peine ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter : https://twitter.com
From playlist Bourbaphy - 17/11/18 - L'information