(PP 6.3) Gaussian coordinates does not imply (multivariate) Gaussian
An example illustrating the fact that a vector of Gaussian random variables is not necessarily (multivariate) Gaussian.
From playlist Probability Theory
Random variables, means, variance and standard deviations | Probability and Statistics
We introduce the idea of a random variable X: a function on a probability space. Associated to such a function is something called a probability distribution, which assigns probabilities, say p_1,p_2,...,p_n to the various possible values of X, say x_1,x_2,...,x_n. The probabilities p_i h
From playlist Probability and Statistics: an introduction
(PP 5.1) Multiple discrete random variables
(0:00) Definition of a random vector. (1:50) Definition of a discrete random vector. (2:28) Definition of the joint PMF of a discrete random vector. (7:00) Functions of random vectors. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=
From playlist Probability Theory
(PP 6.7) Geometric intuition for the multivariate Gaussian (part 2)
How to visualize the effect of the eigenvalues (scaling), eigenvectors (rotation), and mean vector (shift) on the density of a multivariate Gaussian.
From playlist Probability Theory
(PP 6.6) Geometric intuition for the multivariate Gaussian (part 1)
How to visualize the effect of the eigenvalues (scaling), eigenvectors (rotation), and mean vector (shift) on the density of a multivariate Gaussian.
From playlist Probability Theory
(PP 6.1) Multivariate Gaussian - definition
Introduction to the multivariate Gaussian (or multivariate Normal) distribution.
From playlist Probability Theory
Probability DISTRIBUTIONS for Discrete Random Variables (9-3)
A Probability Distribution: a mathematical description of (a) all possible outcomes for a random variable, and (b) the probabilities of each outcome occurring. Can be tabular (i.e., frequency table) or graphical (i.e., bar chart or histogram). For a discrete random variable, the underlying
From playlist Discrete Probability Distributions in Statistics (WK 9 - QBA 237)
Learn to find the or probability from a tree diagram
👉 Learn how to find the conditional probability of an event. Probability is the chance of an event occurring or not occurring. The probability of an event is given by the number of outcomes divided by the total possible outcomes. Conditional probability is the chance of an event occurring
From playlist Probability
Prob & Stats - Random Variable & Prob Distribution (1 of 53) Random Variable
Visit http://ilectureonline.com for more math and science lectures! In this video I will define and gives an example of what is a random variable. Next video in series: http://youtu.be/aEB07VIIfKs
From playlist iLecturesOnline: Probability & Stats 2: Random Variable & Probability Distribution
Lecture 2 | Quantum Entanglements, Part 1 (Stanford)
Lecture 2 of Leonard Susskind's course concentrating on Quantum Entanglements (Part 1, Fall 2006). Recorded October 2, 2006 at Stanford University. This Stanford Continuing Studies course is the first of a three-quarter sequence of classes exploring the "quantum entanglements" in modern
From playlist Course | Quantum Entanglements: Part 1 (Fall 2006)
Lecture 6 | Quantum Entanglements, Part 1 (Stanford)
Lecture 6 of Leonard Susskind's course concentrating on Quantum Entanglements (Part 1, Fall 2006). Recorded October 30, 2006 at Stanford University. This Stanford Continuing Studies course is the first of a three-quarter sequence of classes exploring the "quantum entanglements" in moder
From playlist Course | Quantum Entanglements: Part 1 (Fall 2006)
Can Probability be Negative? | Quantum Mechanics | Quantum Computing
Can a probability be negative? Sounds outlandish, right? We start from there and end with an introduction of quantum computing, showing how negative probability amplitudes lie at the heart of quantum supremacy. #negativeprobability #quantum #quantumcomputing #quantummechanics #quantumsup
From playlist Summer of Math Exposition Youtube Videos
Deep Learning Lecture 10.3 - Restricted Boltzmann Machines
Restricted Boltzmann Machines: - Architecture - Energy - Gibbs Sampling and Contrastive Divergence
From playlist Deep Learning Lecture
Lecture 2 | The Theoretical Minimum
January 16, 2012 - In this course, world renowned physicist, Leonard Susskind, dives into the fundamentals of classical mechanics and quantum physics. He discovers the link between the two branches of physics and ultimately shows how quantum mechanics grew out of the classical structure. I
From playlist Lecture Collection | The Theoretical Minimum: Quantum Mechanics
Geometric Approach to Invertibility of Random Matrices (Lecture 1) by Mark Rudelson
PROGRAM: TOPICS IN HIGH DIMENSIONAL PROBABILITY ORGANIZERS: Anirban Basak (ICTS-TIFR, India) and Riddhipratim Basu (ICTS-TIFR, India) DATE & TIME: 02 January 2023 to 13 January 2023 VENUE: Ramanujan Lecture Hall This program will focus on several interconnected themes in modern probab
From playlist TOPICS IN HIGH DIMENSIONAL PROBABILITY
Lecture 2 | Modern Physics: Quantum Mechanics (Stanford)
Lecture 2 of Leonard Susskind's Modern Physics course concentrating on Quantum Mechanics. Recorded January 21, 2008 at Stanford University. This Stanford Continuing Studies course is the second of a six-quarter sequence of classes exploring the essential theoretical foundations of mode
From playlist Quantum Mechanics Prof. Susskind & Feynman
Lecture 6 | Modern Physics: Quantum Mechanics (Stanford)
Lecture 6 of Leonard Susskind's Modern Physics course concentrating on Quantum Mechanics. Recorded February 18, 2008 at Stanford University. This Stanford Continuing Studies course is the second of a six-quarter sequence of classes exploring the essential theoretical foundations of mod
From playlist Course | Modern Physics: Quantum Mechanics
Lecture 5 | Modern Physics: Quantum Mechanics (Stanford)
Lecture 5 of Leonard Susskind's Modern Physics course concentrating on Quantum Mechanics. Recorded February 11, 2008 at Stanford University. This Stanford Continuing Studies course is the second of a six-quarter sequence of classes exploring the essential theoretical foundations of mod
From playlist Course | Modern Physics: Quantum Mechanics
(PP 3.4) Random Variables with Densities
(0:00) Probability density function (PDF). (3:20) Indicator functions. (5:00) Examples of random variables with densities: Uniform, Exponential, Beta, Normal/Gaussian. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=17567A1A3F5
From playlist Probability Theory
Quantum Mechanics Concepts: 2 Photon Polarisation (continued)
Part 2 of a series: continues photon polarisation
From playlist Quantum Mechanics