CosmicAI Hybrid Seminar Series

Title: Some advances in simulation-based inference: Calibration, aggregation, and model check
Presenter: Dr. Yuling Yao, Assistant Professor in the Department of Statistics and Data Sciences at the University of Texas at Austin. yulingyao.com. 
Venue: POB 4.304 (UT Austin)
Join Zoom Meeting: utexas.zoom.us/j/95502700680 (Meeting ID: 955 0270 0680, Passcode: 745761)

Abstract: Simulation is a powerful way to specify models in modern scientific computing, while the likelihood-free setting imposes challenges for inference and calibration. To start, I present a cosmology example of galaxy clustering analysis using simulation-based inference and normalizing flows. I present three recent advances in simulation-based inference:

(1) “discriminative calibration” develops a general classifier approach to check Bayesian computation including simulation-based inference and Markov chain Monte Carlo. The classifier performance is a consistent estimate of a family of divergence measures, including the classical classifier two-sample test as a special case.
(2) To incorporate posterior approximations from different inference algorithms or flow architectures and improve the final inference quality, I present “simulation-based stacking”,  a general framework to combine probabilistic inferences.
(3) Yet even when the inference is perfect, the simulation model is often an approximation to the nature. I present “simulation-based posterior predictive check”, a framework to check if the simulation model does a good job of capturing relevant aspects of the data, such as means, standard deviations, and quantiles. This new predictive check p-value is ensured to be frequentist-calibrated under the null, making it particularly suitable for rigorously testing scientific hypothesis.