(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
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
Yuxin Chen: "Bayesian Experimental Design in the Physical Sciences"
Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Bayesian Experimental Design in the Physical Sciences" Yuxin Chen - University of Chicago Abstract: How can we intelligently acquire information for decision making,
From playlist Machine Learning for Physics and the Physics of Learning 2019
(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 13.6) Graphical model for Bayesian linear regression
As an example, we write down the graphical model for Bayesian linear regression. We introduce the "plate notation", and the convention of shading random variables which are being conditioned on.
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
Kerrie Mengersen: Bayesian Modelling
Abstract: This tutorial will be a beginner’s introduction to Bayesian statistical modelling and analysis. Simple models and computational tools will be described, followed by a discussion about implementing these approaches in practice. A range of case studies will be presented and possibl
From playlist Probability and Statistics
Eldad Haber: "Active learning and experimental design - who should we test?"
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop II: PDE and Inverse Problem Methods in Machine Learning "Active learning and experimental design - who should we test?" Eldad Haber - University of British Columbia Abstract: Active learning is a branch in machine learning that uses a j
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
Peter Frazier: "Accelerating Scientific Discovery through Interpretable Machine Learning and Int..."
Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Accelerating Scientific Discovery through Interpretable Machine Learning and Intelligent Experimentation" Peter Frazier, Cornell University Abstract: Historically, the
From playlist Machine Learning for Physics and the Physics of Learning 2019
[Phoenics] A Bayesian Optimizer for Chemistry | AISC Author Speaking
For more details including paper and slides, visit https://aisc.a-i.science/events/2019-04-18/
From playlist Machine Learning for Scientific Discovery
DDPS | Bayesian Optimization: Exploiting Machine Learning Models, Physics, & Throughput Experiments
We report new paradigms for Bayesian Optimization (BO) that enable the exploitation of large-scale machine learning models (e.g., neural nets), physical knowledge, and high-throughput experiments. Specifically, we present a paradigm that decomposes the performance function into a reference
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Keynote: AI for Adaptive Experiment Design - Yisong Yue - 10/25/2019
AI-4-Science Workshop, October 25, 2019 at Bechtel Residence Dining Hall, Caltech. Learn more about: - AI-4-science: https://www.ist.caltech.edu/ai4science/ - Events: https://www.ist.caltech.edu/events/ Produced in association with Caltech Academic Media Technologies. ©2019 California I
From playlist AI-4-Science Workshop
Statistical Rethinking 2023 - 01 - The Golem of Prague
Full course details at https://github.com/rmcelreath/stat_rethinking_2023 Chapters: 00:00 Introduction 03:30 DAGs (causal models) 17:50 Golems (stat models) 43:06 Owls (workflow) Intro music: https://www.youtube.com/watch?v=9yHZdLswArc
From playlist Statistical Rethinking 2023
Statistical Rethinking Fall 2017 - week01 lecture01
Week 01, lecture 01 for Statistical Rethinking: A Bayesian Course with Examples in R and Stan, taught at MPI-EVA in Fall 2017. This lecture covers Chapters 1 and 2. Slides are available here: https://speakerdeck.com/rmcelreath Additional information on textbook and R package here: http
From playlist Statistical Rethinking Fall 2017
(ML 7.2) Aspects of Bayesian inference
An informal overview of Bayesian inference, Bayesian procedures, Objective versus Subjective Bayes, Pros/Cons of a Bayesian approach, and priors.
From playlist Machine Learning
How to run A/B Tests as a Data Scientist!
Let's learn about how & why you should use Bayesian Testing. And some advantages of the Bayesian approach over frequentist approach with REAL data/code. Note: Bayesian Appraoch isn't necessarily better in every way - it is another perspective of looking at data. CODE: https://github.com/a
From playlist A/B Testing
SDS 607: Inferring Causality — with Jennifer Hill
#DataScience #CausalInference #BayesianStatistics We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science. This episode is brought to you by Pachyderm
From playlist Super Data Science Podcast
Robust Design Discovery and Exploration in Bayesian Optimization
A Google TechTalk, presented by Ilija Bogunovic, 2022/10/04 BayesOpt Speaker Series - ABSTRACT: Whether in biological design, causal discovery, material production, or physical sciences, one often faces decisions regarding which new data to collect or which experiments to perform. There is
From playlist Google BayesOpt Speaker Series 2021-2022