Expected Value of the Exponential Distribution | Exponential Random Variables, Probability Theory
What is the expected value of the exponential distribution and how do we find it? In today's video we will prove the expected value of the exponential distribution using the probability density function and the definition of the expected value for a continuous random variable. It's gonna b
From playlist Probability Theory
From playlist Courses and Series
Expected Value of the Bernoulli Distribution | Probability Theory
How do we derive the mean or expected value of a Bernoulli random variable? We'll be going over that in today's probability theory lesson! Remember a Bernoulli random variable is a random variable that is equal to 1 (success) with probability p and equal to 0 (failure) with probability 1-
From playlist Probability Theory
12 Stochastic Gradient Estimators
Slides and more information: https://mml-book.github.io/slopes-expectations.html
From playlist There and Back Again: A Tale of Slopes and Expectations (NeurIPS-2020 Tutorial)
Expected Value of a Discrete Random Variable | Probability Theory, Mean of a Random Variable
Do you want to know how to find the expected value of a discrete random variable? You're in the right place! The expected value of a discrete random variable is also often called the mean of the discrete random variable. This is the topic of today's probability theory lesson! To find the
From playlist Probability Theory
(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
Unit 2 - consumer mistakes part 1
From playlist Courses and Series
From playlist COMP0168 (2020/21)
Typical Decoding for Natural Language Generation (Get more human-like outputs from language models!)
#deeplearning #nlp #sampling Modern language models like T5 or GPT-3 achieve remarkably low perplexities on both training and validation data, yet when sampling from their output distributions, the generated text often seems dull and uninteresting. Various workarounds have been proposed,
From playlist Papers Explained
MIT 14.01 Principles of Microeconomics, Fall 2018 Instructor: Prof. Jonathan Gruber View the complete course: https://ocw.mit.edu/14-01F18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62oJSoqb4Rf-vZMGUBe59G- This video explains the economic concept of decision making
From playlist MIT 14.01 Principles of Microeconomics, Fall 2018
William Wen: "Bayesian Statistics and its Application to Integrative Statistical Genomics"
Computational Genomics Summer Institute 2016 "Bayesian Statistics and its Application to Integrative Statistical Genomics" Xiaoquan (William) Wen, University of Michigan Institute for Pure and Applied Mathematics, UCLA July 18, 2016 For more information: http://computationalgenomics.bio
From playlist Computational Genomics Summer Institute 2016
SPSS Tutorial for data analysis | SPSS for Beginners | Part 2
SPSS Statistics is a software package used for interactive, or batched, statistical analysis. Long produced by SPSS Inc., it was acquired by IBM in 2009. The current versions are named IBM SPSS Statistics. In this course you will how to use SPSS for data analysis. This #SPSS course is begi
From playlist SPSS data Analysis
MIT 6.S897 Machine Learning for Healthcare, Spring 2019 Instructor: Peter Szolovits View the complete course: https://ocw.mit.edu/6-S897S19 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j Prof. Szolovits explains differential diagnosis, the disti
From playlist MIT 6.S897 Machine Learning for Healthcare, Spring 2019
Lecture 3.4: Laura Schulz - Childrens' Sensitivity to Cost and Value of Information
MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: Laura Schulz Much like scientists, children make judgements about the cost and value of information, learn from statistical evidence, use interpretat
From playlist MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015
DRAM Errors in the Wild: A Large-Scale Field Study
(October 21, 2009) Bianca Schroeder of the University of Toronto Computer Science Department gives an in depth discussion on how common dynamic random access memory errors are, their statistical properties, and how they are affected by external and chip-specific factors. Stanford Univer
From playlist Engineering
Maxim Ziatdinov - Bits and Atoms: Exploring the Intersection of Machine Learning and Microscopy
Recorded 27 January 2023. Maxim Ziatdinov of the Oak Ridge National Laboratory presents "Bits and Atoms: Exploring the Intersection of Machine Learning and Microscopy" at IPAM's Learning and Emergence in Molecular Systems Workshop. Abstract: Machine learning and artificial intelligence are
From playlist 2023 Learning and Emergence in Molecular Systems
Nexus trimester - Sewoong Oh (University of Illinois)
Privacy Region and its Applications Sewoong Oh (University of Illinois) March 31, 2016 Abstract: Interactive querying of a database degrades the privacy level. In this paper we answer the fundamental question of characterizing the level of differential privacy degradation as a function of
From playlist Nexus Trimester - 2016 - Secrecy and Privacy Theme
OCR MEI Statistics Minor C: PMCC Hypothesis Testing: 09 Effect Sizes
https://www.buymeacoffee.com/TLMaths Navigate all of my videos at https://sites.google.com/site/tlmaths314/ Like my Facebook Page: https://www.facebook.com/TLMaths-1943955188961592/ to keep updated Follow me on Instagram here: https://www.instagram.com/tlmaths/ Many, MANY thanks to Dea
From playlist OCR MEI Statistics Minor C: PMCC Hypothesis Testing
Brief Introduction to Statistical Inference - Causal Inference
In this video, I briefly introduce the topic of Statistical Inference and go over its most fundamental concepts - those that we will use in this series. If you want to learn more about this stuff, check out this link to my entire series on Statistical Inference: https://www.youtube.com/pla
From playlist Causal Inference - The Science of Cause and Effect