Bayesian vs frequentist statistics probability - part 1
This video provides an intuitive explanation of the difference between Bayesian and classical frequentist statistics. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Unfo
From playlist Bayesian statistics: a comprehensive course
16 Sequential Bayes: Data order invariance
A proof of the fact that for independent sequences of data, the order which they are received does not affect the posterior distribution; and hence does not affect inference. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.
From playlist Bayesian statistics: a comprehensive course
Bayesian vs frequentist statistics
This video provides an intuitive explanation of the difference between Bayesian and classical frequentist statistics. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Un
From playlist Bayesian statistics: a comprehensive course
An introduction to the use of Bayes' rule in statistics. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Unfortunately, Ox Educ is no more. Don't fret however as a whol
From playlist Bayesian statistics: a comprehensive course
Bayesian vs frequentist statistics probability - part 2
This video provides a short introduction to the similarities and differences between Bayesian and Frequentist views on probability. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdi
From playlist Bayesian statistics: a comprehensive course
Tim Sullivan: Brittleness and robustness of Bayesian inference for complex systems
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Numerical Analysis and Scientific Computing
(ML 7.1) Bayesian inference - A simple example
Illustration of the main idea of Bayesian inference, in the simple case of a univariate Gaussian with a Gaussian prior on the mean (and known variances).
From playlist Machine Learning
Conditional Probability: Bayes’ Theorem – Disease Testing (Table and Formula)
This video shows how to determine conditional probability using a table and using Bayes' theorem. @mathipower4u
From playlist Probability
Comparing Bayesian optimization with traditional sampling
Welcome to video #2 of the Adaptive Experimentation series, presented by graduate student Sterling Baird @sterling-baird at the 18th IEEE Conference on eScience in Salt Lake City, UT (Oct 10-14, 2022). In this video Sterling introduces Bayesian Optimization as an alternative method for sa
From playlist Optimization tutorial
PB2 - Population-Based Bandit Optimization
Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-15th-2021-a68f599395e3428c878dc74c5f0e1124 Chapters 0:00 Introduction 2:41 Hyperparameter Optimization 3:44 Population-Based Training 6:12 Evolution + Bayesian Optimization 8:54 ASHA 10:48 Results Thanks
From playlist AI Weekly Update - July 15th, 2021!
6 - Bayes' rule in inference - likelihood
Provides an introduction to Bayesian statistics - in particular the likelihood - by running through a simple example of the application of Bayes' rule to the case of inference over a binary parameter, If you are interested in seeing more of the material, arranged into a playlist, please v
From playlist Bayesian statistics: a comprehensive course
Lecture 10D : Making full Bayesian learning practical
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 10D : Making full Bayesian learning practical
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Efficient sampling through variable splitting-inspired (...) - Chainais - Workshop 2 - CEB T1 2019
Pierre Chainais (Ecole Centrale Lille) / 12.03.2019 Efficient sampling through variable splitting-inspired bayesian hierarchical models. Markov chain Monte Carlo (MCMC) methods are an important class of computation techniques to solve Bayesian inference problems. Much research has been
From playlist 2019 - T1 - The Mathematics of Imaging
Lecture 10/16 : Combining multiple neural networks to improve generalization
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 10A Why it helps to combine models 10B Mixtures of Experts 10C The idea of full Bayesian learning 10D Making full Bayesian learning practical 10E Dropout: an efficient way to combine neural nets
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Lecture 10.4 — Making full Bayesian learning practical [Neural Networks for Machine Learning]
Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-2012-001
From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton
Nineteenth Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series Talk
Date: Wednesday, March 24, 2021, 10:00am Eastern Time Zone (US & Canada) Speaker: Marcelo Pereyra, Heriot-Watt University Abstract: Play & Play (PnP) methods have become ubiquitous in Bayesian imaging. These methods derive Minimum Mean Square Error (MMSE) or Maximum A Posteriori (MAP) es
From playlist Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series
Andreas Krause: "Safe and Efficient Exploration in Reinforcement Learning"
Intersections between Control, Learning and Optimization 2020 "Safe and Efficient Exploration in Reinforcement Learning" Andreas Krause - ETH Zurich Abstract: At the heart of Reinforcement Learning lies the challenge of trading exploration -- collecting data for learning better models --
From playlist Intersections between Control, Learning and Optimization 2020
Automated Deep Learning: Joint Neural Architecture and Hyperparameter Search (algorithm) | AISC
Toronto Deep Learning Series, 10 December 2018 Paper: https://arxiv.org/abs/1807.06906 Discussion Lead: Mark Donaldson (Ryerson University) Discussion Facilitator: Masoud Hashemi (RBC) Host: Shopify Date: Dec 10th, 2018 Towards Automated Deep Learning: Efficient Joint Neural Architectu
From playlist Architecture Tuning
1 - Marginal probability for continuous variables
This explains what is meant by a marginal probability for continuous random variables, how to calculate marginal probabilities and the graphical intuition behind the method. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.c
From playlist Bayesian statistics: a comprehensive course