Convergence (mathematics) | Stochastic processes

Convergence of random variables

In probability theory, there exist several different notions of convergence of random variables. The convergence of sequences of random variables to some limit random variable is an important concept in probability theory, and its applications to statistics and stochastic processes. The same concepts are known in more general mathematics as stochastic convergence and they formalize the idea that a sequence of essentially random or unpredictable events can sometimes be expected to settle down into a behavior that is essentially unchanging when items far enough into the sequence are studied. The different possible notions of convergence relate to how such a behavior can be characterized: two readily understood behaviors are that the sequence eventually takes a constant value, and that values in the sequence continue to change but can be described by an unchanging probability distribution. (Wikipedia).

Convergence of random variables
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Find the Interval of Convergence

How to find the interval of convergence for a power series using the root test.

From playlist Convergence (Calculus)

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L18.6 Convergence in Probability

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

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S18.1 Convergence in Probability of the Sum of Two Random Variables

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

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Calculus: How Convergence Explains The Limit

The limit definition uses the idea of convergence twice (in two slightly different ways). Once the of convergence is grasped, the limit concept becomes easy, even trivial. This clip explains convergence and shows how it can be used to under the limit.

From playlist Summer of Math Exposition Youtube Videos

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Interval of Convergence (silent)

Finding the interval of convergence for power series

From playlist 242 spring 2012 exam 3

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Newton's Method Interval of Convergence

How to find the Interval of Convergence for Newton-type methods such as Newton's Method, Secant Method, and Finite Difference Method including discussion on Damped Newton's Method and widening the convergence interval. Example code in R hosted on Github: https://github.com/osveliz/numerica

From playlist Root Finding

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The Law of Large Numbers - Explained

The law of large numbers is one of the most intuitive ideas in statistics, however, often the strong and weak versions of the law can be difficult to understand. In this video, I breakdown what the definitions of both laws mean and use this as a way to introduce the concepts of convergence

From playlist Summer of Math Exposition 2 videos

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Math 131 111416 Sequences of Functions: Pointwise and Uniform Convergence

Definition of pointwise convergence. Examples, nonexamples. Pointwise convergence does not preserve continuity, differentiability, or integrability, or commute with differentiation or integration. Uniform convergence. Cauchy criterion for uniform convergence. Weierstrass M-test to imp

From playlist Course 7: (Rudin's) Principles of Mathematical Analysis

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Infinity Paradox -- Riemann series theorem

Absolute Convergence versus Conditional Convergence

From playlist Physics

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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 Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 6.262 Discrete Stochastic Processes, Spring 2011

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L18.8 Related Topics

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

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Random Matrices and Their Limits - R. Speicher - Workshop 2 - CEB T3 2017

Roland Speicher / 26.10.17 Random Matrices and Their Limits The free probability perspective on random matrices is that the large size limit of random matrices is given by some (usually interesting) operators on Hilbert spaces and corresponding operator algebras. The prototypical example

From playlist 2017 - T3 - Analysis in Quantum Information Theory - CEB Trimester

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10. Renewals and the Strong Law of Large Numbers

MIT 6.262 Discrete Stochastic Processes, Spring 2011 View the complete course: http://ocw.mit.edu/6-262S11 Instructor: Robert Gallager License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 6.262 Discrete Stochastic Processes, Spring 2011

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Adam Jakubowski: Functional convergence for dependent heavy-tailed models

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 Probability and Statistics

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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 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013

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L18.7 Convergence in Probability Examples

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

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David Kelly: Fast slow systems with chaotic noise

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 Probability and Statistics

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The Difference Between Pointwise Convergence and Uniform Convergence

Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys The Difference Between Pointwise Convergence and Uniform Convergence

From playlist Advanced Calculus

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