Markov processes

Nearly completely decomposable Markov chain

In probability theory, a nearly completely decomposable (NCD) Markov chain is a Markov chain where the state-space can be partitioned in such a way that movement within a partition occurs much more frequently than movement between partitions. Particularly efficient algorithms exist to compute the stationary distribution of Markov chains with this property. (Wikipedia).

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Markov Chains Clearly Explained! Part - 1

Let's understand Markov chains and its properties with an easy example. I've also discussed the equilibrium state in great detail. #markovchain #datascience #statistics For more videos please subscribe - http://bit.ly/normalizedNERD Markov Chain series - https://www.youtube.com/playl

From playlist Markov Chains Clearly Explained!

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Markov Chains: Recurrence, Irreducibility, Classes | Part - 2

Let's understand Markov chains and its properties. In this video, I've discussed recurrent states, reducibility, and communicative classes. #markovchain #datascience #statistics For more videos please subscribe - http://bit.ly/normalizedNERD Markov Chain series - https://www.youtube.c

From playlist Markov Chains Clearly Explained!

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Prob & Stats - Markov Chains (27 of 38) Absorbing Markov Chain: Stable Matrix=? Ex. 2

Visit http://ilectureonline.com for more math and science lectures! In this video I will find the stable transition matrix (4x4) in an absorbing Markov chain. Next video in the Markov Chains series: http://youtu.be/u89Sd514EDI

From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes

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Prob & Stats - Markov Chains (25 of 38) Absorbing Markov Chain: Stable Matrix=?

Visit http://ilectureonline.com for more math and science lectures! In this video I will find the stable transition matrix in an absorbing Markov chain. Next video in the Markov Chains series: http://youtu.be/72Ipee3ueUs

From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes

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Hidden Markov Model Clearly Explained! Part - 5

So far we have discussed Markov Chains. Let's move one step further. Here, I'll explain the Hidden Markov Model with an easy example. I'll also show you the underlying mathematics. #markovchain #datascience #statistics For more videos please subscribe - http://bit.ly/normalizedNERD Mar

From playlist Markov Chains Clearly Explained!

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Prob & Stats - Markov Chains (28 of 38) Absorbing Markov Chain: Stable Distribution Matrix=?

Visit http://ilectureonline.com for more math and science lectures! In this video I will find the stable distribution matrix after finding stable transition matrix. Next video in the Markov Chains series: http://youtu.be/0iNR2_gCM7I

From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes

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Prob & Stats - Markov Chains (22 of 38) Absorbing Markov Chains - Example 2

Visit http://ilectureonline.com for more math and science lectures! In this video I will find the stable transition matrix in an absorbing Markov chain. Next video in the Markov Chains series: http://youtu.be/hMceS_HIcKY

From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes

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Prob & Stats - Markov Chains (10 of 38) Regular Markov Chain

Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is a regular Markov chain. Next video in the Markov Chains series: http://youtu.be/DeG8MlORxRA

From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes

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Directed Laplacian Matrices - John Peebles

Short Talks by Postdoctoral Members Topic: Directed Laplacian Matrices Speaker: John Peebles Affiliation: Member, School of Mathematics Date: September 28, 2021

From playlist Mathematics

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Regenerative Stochastic Processes by Krishna Athreya

PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear

From playlist Advances in Applied Probability 2019

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Macdonald processes I - Alexei Borodin

Alexei Borodin Massachussetts Institute of Technology October 8, 2013 Our goal is to explain how certain basic representation theoretic ideas and constructions encapsulated in the form of Macdonald processes lead to nontrivial asymptotic results in various `integrable'; probabilistic probl

From playlist Mathematics

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Prob & Stats - Markov Chains (21 of 38) Absorbing Markov Chains - Example 1

Visit http://ilectureonline.com for more math and science lectures! In this video I will find the stable distribution matrix in an absorbing Markov chain. Next video in the Markov Chains series: http://youtu.be/1bErNmzD8Sw

From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes

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Solving Laplacian Systems of Directed Graphs - John Peebles

Computer Science/Discrete Mathematics Seminar II Topic: Solving Laplacian Systems of Directed Graphs Speaker: John Peebles Affiliation: Member, School of Mathematics Date: March 02, 2021 For more video please visit http://video.ias.edu

From playlist Mathematics

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High dimensional expanders - Part 2 - Irit Dinur

Computer Science/Discrete Mathematics Seminar II Topic: High dimensional expanders - Part 2 Speaker: Irit Dinur Affiliation: Weizmann Institute of Science; Visiting Professor, School of Mathematics Date: March 24, 2020 For more video please visit http://video.ias.edu

From playlist Mathematics

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Macdonald processes II - Alexei Borodin

Alexei Borodin Massachussetts Institute of Technology October 9, 2013 Our goal is to explain how certain basic representation theoretic ideas and constructions encapsulated in the form of Macdonald processes lead to nontrivial asymptotic results in various `integrable'; probabilistic probl

From playlist Mathematics

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The Rule 54: Exactly solvable deterministic interacting model of transport by Tomaz Prosen

PROGRAM THERMALIZATION, MANY BODY LOCALIZATION AND HYDRODYNAMICS ORGANIZERS: Dmitry Abanin, Abhishek Dhar, François Huveneers, Takahiro Sagawa, Keiji Saito, Herbert Spohn and Hal Tasaki DATE : 11 November 2019 to 29 November 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore How do is

From playlist Thermalization, Many Body Localization And Hydrodynamics 2019

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Gabriela Ciolek - Sharp Bernstein and Hoeffding type inequalities for regenerative Markov chains

The purpose of this talk is to present Bernstein and Hoeffding type functional inequalities for regenerative Markov chains. Furthermore, we generalize these results and show exponential bounds for suprema of empirical processes over a class of functions F which size is controlled by its un

From playlist Les probabilités de demain 2017

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Joshua Bon - Twisted: Improving particle filters by learning modified paths

Dr Joshua Bon (QUT) presents "Twisted: Improving particle filters by learning modified paths", 22 April 2022.

From playlist Statistics Across Campuses

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(ML 18.4) Examples of Markov chains with various properties (part 1)

A very simple example of a Markov chain with two states, to illustrate the concepts of irreducibility, aperiodicity, and stationary distributions.

From playlist Machine Learning

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Markov processes and applications-2 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

Related pages

Stochastic matrix | Lumpability | Probability theory | Stationary distribution | Markov chain