Adventures in Practical Population Inference - IPAM at UCLA
Offered By: Institute for Pure & Applied Mathematics (IPAM) via YouTube
Course Description
Overview
Explore the challenges and advancements in population inference for gravitational-wave astronomy in this 43-minute conference talk by Colm Talbot from the Massachusetts Institute of Technology. Dive into the computational complexities of extracting astrophysical and cosmological information from gravitational-wave observations, focusing on compact binary populations. Examine commonly used methods for astrophysical inference and their limitations. Discover novel approaches to mitigate these issues, including analytical integration, Monte Carlo integration, and density estimation techniques. Learn about selection effects, the observation-biased likelihood, and methods for evaluating selection functions. Gain insights into uncertainty quantification, scaled Gaussian mixture models, and continuous representations using Gaussian process regression and neural networks. Understand the challenges of comparing observations with predictions and explore potential solutions for more efficient analysis of growing gravitational-wave catalogs.
Syllabus
Outline
Definitions
Single event posterior distribution
Selection effects The observation biased likelihood
Integration methods An aside
Analytic integration
Monte Carlo integration Uncertainty
Evaluating the selection function
Putting it together Uncertainties
Density estimation Methods
Scaled Gaussian Mixture Model
Continuous representations Methods
Gaussian process regression
Neural networks
Why don't we just remove the MC integrals?
Comparing observations with predictions
Summary
Taught by
Institute for Pure & Applied Mathematics (IPAM)
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