(ML 11.8) Bayesian decision theory
Choosing an optimal decision rule under a Bayesian model. An informal discussion of Bayes rules, generalized Bayes rules, and the complete class theorems.
From playlist Machine Learning
(ML 12.4) Bayesian model selection
Approaches to model selection from a Bayesian perspective: Bayesian model averaging (BMA), "Type II MAP", and Type II Maximum Likelihood (a.k.a. ML-II, a.k.a. the evidence approximation, a.k.a. empirical Bayes).
From playlist Machine Learning
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
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
(ML 11.4) Choosing a decision rule - Bayesian and frequentist
Choosing a decision rule, from Bayesian and frequentist perspectives. To make the problem well-defined from the frequentist perspective, some additional guiding principle is introduced such as unbiasedness, minimax, or invariance.
From playlist Machine Learning
Supercharging Decision Making with Bayes
Bayesian Decision Theory is a fundamental statistical approach to the problem of pattern classification. It is considered as the ideal pattern classifier and often used as the benchmark for other algorithms because its decision rule automatically minimizes its loss function. PUBLICATION P
From playlist Machine Learning
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
What is a marginal probability?
An introduction to the concept of marginal probabilities, via the use of a simple 2 dimensional discrete example. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm For mo
From playlist Bayesian statistics: a comprehensive course
Bayesian Optimization in the Wild: Risk-Averse Decisions and Budget Constraints
A Google TechTalk, presented by Anastasia Makarova, 2022/08/23 Google BayesOpt Speaker Series - ABSTRACT: Black-box optimization tasks frequently arise in high-stakes applications such as material discovery or hyperparameter tuning of complex systems. In many of these applications, there i
From playlist Google BayesOpt Speaker Series 2021-2022
Chaitanya Swamy: Signaling in Bayesian Games
We study the optimization problem faced by an informed principal in a Bayesian game, who can reveal some information about the underlying random state of nature to the players (thereby influencing their payoffs) so as to obtain a desirable equilibrium. This yields the following signaling p
From playlist HIM Lectures: Trimester Program "Combinatorial Optimization"
The Unreasonable Effectiveness of Bayesian Prediction
My Patreon : https://www.patreon.com/user?u=49277905 Icon References : https://www.flaticon.com/authors/srip
From playlist Bayesian Statistics
Uncertainty in Visuomotor Behavior by Konrad Kording
PROGRAM ICTP-ICTS WINTER SCHOOL ON QUANTITATIVE SYSTEMS BIOLOGY (ONLINE) ORGANIZERS: Vijaykumar Krishnamurthy (ICTS-TIFR, India), Venkatesh N. Murthy (Harvard University, USA), Sharad Ramanathan (Harvard University, USA), Sanjay Sane (NCBS-TIFR, India) and Vatsala Thirumalai (NCBS-TIFR,
From playlist ICTP-ICTS Winter School on Quantitative Systems Biology (ONLINE)
Rémi Bardenet: A tutorial on Bayesian machine learning: what, why and how - lecture 1
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
Applied Machine Learning: Secret Sauce
Professor Jann Spiess shares the secret sauce of applied machine learning.
From playlist Machine Learning & Causal Inference: A Short Course
Quantitative Finance: Toward A General Framework for Modelling Roll-Over Risk
SIAM Activity Group on FME Virtual Talk Series Join us for a series of online talks on topics related to mathematical finance and engineering and running every two weeks until further notice. The series is organized by the SIAM Activity Group on Financial Mathematics and Engineering. Spea
From playlist SIAM Activity Group on FME Virtual Talk Series
AI Weekly Update - May 11th, 2020 (#20)
Thank you for watching! Please Subscribe! Machine Learning Street Talk: https://www.youtube.com/channel/UCMLtBahI5DMrt0NPvDSoIRQ Paper Links: Deep Learning with Graph-Structured Representations: https://dare.uva.nl/search?identifier=1b63b965-24c4-4bcd-aabb-b849056fa76d Yoshua Bengio ICLR
From playlist AI Research Weekly Updates
(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
Lecture 11 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms. This course provides a broad introdu
From playlist Lecture Collection | Machine Learning
Unit 5 - pareto optimal allocations part 3
From playlist Courses and Series