Past Recording
Share
Star()
High Dimensional Inference in the Universe
Tuesday Mar 9 2021 17:00 GMT
Please to join the live chat.
High Dimensional Inference in the Universe
Why This Is Interesting

High-dimensional probability density estimation for inference suffers from the “curse of dimensionality”. For many physical inference problems, the full posterior distribution is unwieldy and seldom used in practice. Instead, we propose direct estimation of lower-dimensional marginal distributions, bypassing high-dimensional density estimation or high-dimensional Markov chain Monte Carlo (MCMC) sampling. By evaluating the two-dimensional marginal posteriors we can unveil the full-dimensional parameter covariance structure. We additionally propose constructing a simple hierarchy of fast neural regression models, called Moment Networks, that compute increasing moments of any desired lower-dimensional marginal posterior density; these reproduce exact results from analytic posteriors and those obtained from Masked Autoregressive Flows. We demonstrate marginal posterior density estimation using high-dimensional LIGO-like gravitational wave time series and describe applications for problems of fundamental cosmology.

Discussion Points
  • what are moment networks?
  • two-dimensional marginal posteriors we can unveil the full-dimensional parameter covariance structure
Time of Recording: Tuesday Mar 9 2021 17:00 GMT