Joshua Speagle - A Brief Introduction to Nested Sampling - IPAM at UCLA
Recorded 17 November 2021. Joshua Speagle of the University of Toronto presents "A Brief Introduction to Nested Sampling" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy. Abstract: Quantifying model uncertainty and performing model selection within a Bayesian framework is becoming an ever-larger part of scientific analysis both within and outside of astronomy. I will present a brief introduction to Nested Sampling, a complementary framework to Markov Chain Monte Carlo approaches that is designed to estimate marginal likelihoods (Bayesian evidences) and posterior distributions, outline some of its pros and cons, and briefly discuss more recent extensions such as Dynamic Nested Sampling. Learn more online at: http://www.ipam.ucla.edu/programs/wor...
Scott Field - Gravitational Wave Parameter Estimation with Compressed Likelihood Evaluations
Michael Betancourt: Scalable Bayesian Inference with Hamiltonian Monte Carlo
How AI Cracked the Protein Folding Code and Won a Nobel Prize
All About that Bayes: Probability, Statistics, and the Quest to Quantify Uncertainty
England – Kroatien Highlights | Gruppe L, FIFA WM 2026 | sportstudio
The algorithm that (eventually) revolutionized statistics - #SoMEpi
The intuition behind the Hamiltonian Monte Carlo algorithm
Building the PERFECT Linux PC with Linus Torvalds
Efficient Bayesian inference with Hamiltonian Monte Carlo -- Michael Betancourt (Part 1)
An introduction to Gibbs sampling
How Bayes Theorem works
Martin Jankowiak - Brief Introduction to Probabilistic Programming
Accept-Reject Sampling : Data Science Concepts
Bayes theorem, the geometry of changing beliefs
A Beginner's Guide to Monte Carlo Markov Chain MCMC Analysis 2016
Importance Sampling
Hamiltonian Monte Carlo For Dummies (Statisticians / Pharmacometricians / All)
Bayesian or Frequentist, Which Are You? By Michael I. Jordan (Part 1 of 2)
Markov Chain Monte Carlo
