Unit 2 - consumer demand part 2
From playlist Courses and Series
Expected Values of Discrete Random Variables - Statistics
We talk about how to find the expected value of discrete random variables given their pmfs. We also talk about Bernoulli random variables in this #statistics and #probability video. Join this channel to get access to perks: https://www.youtube.com/channel/UCGYSfZbPp3BiAFs531PBY7g/join In
From playlist Statistics
From playlist Courses and Series
Unit 2 - consumer demand part 4
From playlist Courses and Series
(PP 4.1) Expectation for discrete random variables
(0:00) Definition of expectation for discrete r.v.s. (4:17) Well-defined expectation. (8:15) E(X) may exist and be infinite. (10:58) E(X) might fail to exist. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=17567A1A3F5DB5E4
From playlist Probability Theory
Expected Values for Continuous Variables!!!
If you ever muck around in statistics, it's not long before you see E(x) = something. These are expected values. Expected Values for Continuous Variables are a little trickier than their discrete counterparts because we have to do some calculus. However, I'll walk you through it, one step
From playlist Statistics Fundamentals
Unit 2 - consumer mistakes part 1
From playlist Courses and Series
(ML 7.7.A2) Expectation of a Dirichlet random variable
How to compute the expected value of a Dirichlet distributed random variable.
From playlist Machine Learning
Rémi Bardenet: A tutorial on Bayesian machine learning: what, why and how - lecture 2
HYBRID EVENT Recorded during the meeting "End-to-end Bayesian Learning Methods " the October 25, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's
From playlist Mathematical Aspects of Computer Science
What's the Use of Utility Functions?
A lot of our problems with AI seem to relate to its utility function. Why do we need one of those, anyway? Footage from The Simpsons, Copyright FOX, used under Fair Use With thanks to everyone who gave their suggestions and feedback about drafts of this video And thanks to my Patreon Sup
From playlist Technical
MIT 14.04 Intermediate Microeconomic Theory, Fall 2020 Instructor: Prof. Robert Townsend View the complete course: https://ocw.mit.edu/courses/14-04-intermediate-microeconomic-theory-fall-2020/ YouTube Playlist: https://www.youtube.com/watch?v=XSTSfCs74bg&list=PLUl4u3cNGP63wnrKge9vllow3Y2
From playlist MIT 14.04 Intermediate Microeconomic Theory, Fall 2020
What Were You Thinking? Decision Theory as Coherence Test - Prof. Itzhak Gilboa
Abstract This talk is based on the joint work with Prof. Larry Samuelson from the Department of Economics at Yale University and full text is available here: https://www.google.com/url?q=https%3A%2F%2Fitzhakgilboa.weebly.com%2Fuploads%2F8%2F3%2F6%2F3%2F8363317%2Fgs_decision_theory_coheren
From playlist Uncertainty and Risk
ML Tutorial: Adversarial and Competitive Methods in Machine Learning (Amos Storkey)
Machine Learning Tutorial at Imperial College London: Adversarial and Competitive Methods in Machine Learning Amos Storkey (University of Edinburgh) October 28, 2015
From playlist Machine Learning Tutorials
Eliezer Yudkowsky – AI Alignment: Why It's Hard, and Where to Start
On May 5, 2016, Eliezer Yudkowsky gave a talk at Stanford University for the 26th Annual Symbolic Systems Distinguished Speaker series (https://symsys.stanford.edu/viewing/event/26580). Eliezer is a senior research fellow at the Machine Intelligence Research Institute, a research nonprofi
From playlist Machine Learning Shorts
Rails Pacific 2016 - Workshop / Taming Chaotic Specs: RSpec Design Patterns by Adam Cuppy
Workshop / Taming Chaotic Specs: RSpec Design Patterns by Adam Cuppy Don’t you hate when testing takes 3x as long because your specs are hard to understand? Following a few simple patterns, you can easily take a bloated spec and make it DRY and simple to extend. We will take a bloated sam
From playlist Rails Pacific 2016
Neuronal Signals for Economic Utility - W. Schultz - 6/1/17
T & C Chen Center for Social and Decision Neuroscience Distinguished Lecture “Neuronal Signals for Economic Utility” Wolfram Schultz, Professor of Neuroscience, University of Cambridge Wolfram Schultz works on the biological basis of reward. He uses behavioral concepts from animal learni
From playlist Talks and Seminars
Self-Improving Artificial Intelligence
October 24, 2007 lecture by Steve Omohundro for the Stanford University Computer Systems Colloquium (EE 380). Steve presents fundamental principles that underlie the operation of "self-improving systems," i.e., computer software and hardware that improve themselves by learning from their o
From playlist Lecture Collection | Computer Systems Laboratory Colloquium (2007-2008)
“Choice Modeling and Assortment Optimization” - Session I - Prof. Huseyin Topaloglu
This module overviews static and dynamic assortment optimization problems. We start with an introduction to discrete choice modeling and discuss estimation issues when fitting a choice model to observed sales histories. Following this introduction, we discuss static and dynamic assortment
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management
More Help with Expected Value of Discrete Random Variables
Additional insight into calculating the mean [expected vale] of joint discrete random variables
From playlist Unit 6 Probability B: Random Variables & Binomial Probability & Counting Techniques
Recorded: Spring 2014 Lecturer: Dr. Erin M. Buchanan Materials: created for Memory and Cognition (PSY 422) using Smith and Kosslyn (2006) Lecture materials and assignments available at statisticsofdoom.com. https://statisticsofdoom.com/page/other-courses/
From playlist PSY 422 Memory and Cognition with Dr. B