Bayesian networks | Markov networks
In statistics and machine learning, when one wants to infer a random variable with a set of variables, usually a subset is enough, and other variables are useless. Such a subset that contains all the useful information is called a Markov blanket. If a Markov blanket is minimal, meaning that it cannot drop any variable without losing information, it is called a Markov boundary. Identifying a Markov blanket or a Markov boundary helps to extract useful features. The terms of Markov blanket and Markov boundary were coined by Judea Pearl in 1988. (Wikipedia).
From playlist BeDMaSH
From playlist 'Sleeping Sun' videos.
R.M.S Olympic - Old Reliable High Definition
The big break for the Olympic Class Liners. Please Read - I do not intend to claim that I own any of the pictures neither the only movie in this clip. 1911 to 1937 Soundtrack - Nightwish Sleeping Sun. I do not intend to claim that I own the soundtrack in this video Content that y
From playlist 'Sleeping Sun' videos.
From playlist 'Sleeping Sun' videos.
Yuval Peres - Breaking barriers in probability
http://www.lesprobabilitesdedemain.fr/index.html Organisateurs : Céline Abraham, Linxiao Chen, Pascal Maillard, Bastien Mallein et la Fondation Sciences Mathématiques de Paris
From playlist Les probabilités de demain 2016
Cover Times, Blanket Times, and Majorizing Measures - James Lee
James Lee University of Washington April 12, 2010 The cover time of a graph is one of the most basic and well-studied properties of the simple random walk, and yet a number of fundamental questions concerning cover times have remained open. We show that there is a deep connection between c
From playlist Mathematics
Factor Graphs 2 - Conditional Independence | Stanford CS221: AI (Autumn 2019)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3BklVrc Topics: Beam search, local search, conditional independence, variable elimination Reid Pryzant, PhD Candidate & Head Course Assistant http://onlinehub.stanf
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019
undergraduate machine learning 7: Bayesian networks, aka probabilistic graphical models
Introduction to Bayesian networks, conditional independence, Markov blankets, inference and explaining away. The slides are available here: http://www.cs.ubc.ca/~nando/340-2012/lectures.php This course was taught in 2012 at UBC by Nando de Freitas
From playlist undergraduate machine learning at UBC 2012
This is my tribute to the first liner within the Olympic class ocean liners by White Star Line, Olympic Music-Sleeping Sun, Nightwish
From playlist 'Sleeping Sun' videos.
From playlist Simulink Design Award: 2013 BEST Robotics
Stereolab "Ticker Tape Of The Unconscious" (Montage)
Taken from the album "Dots And Loops".
From playlist the absolute best of stereolab
Machine Learning Summer School 2014 in Pittsburgh http://www.mlss2014.com See the website for more videos and slides. Nando de Freitas Lecture 3
From playlist Talks and tutorials
Statistical Rethinking - Lecture 11
Lecture 11 - Markov chain Monte Carlo - Statistical Rethinking: A Bayesian Course with R Examples
From playlist Statistical Rethinking Winter 2015
Abstraction - Seminar 1 - Natural Abstraction 1
This seminar series is on the relations among Natural Abstraction, Renormalisation and Resolution. This week Alexander Oldenziel gives the first lecture on the Natural Abstraction track, introducing the topic of agents and how to begin formalising that in terms of Bayesian networks and Mar
From playlist Abstraction
Nexus Trimester - Kamalika Chaudhuri (UC San Diego)
Privacy-preserving Analysis of Correlated data Kamalika Chaudhuri (UC San Diego) March 30, 2016 Abstract: Many modern machine learning applications involve private and sensitive data that are highly correlated. Examples are mining of time series of physical activity measurements, or minin
From playlist Nexus Trimester - 2016 - Secrecy and Privacy Theme
02.10b - ISE2021 - Language Model and N-Grams - 2
Information Service Engineering 2021 Prof. Dr. Harald Sack Karlsruhe Institute of Technology Summer semester 2021 Lecture 4: Natural Language Processing - 2 Language Model and N-Grams - 2 - Language model - N-grams - Document corpora - Markov Assumption - Maximum Likelihood Estimation -
From playlist ISE 2021 - Lecture 04, 05.05.2021