An Introduction to Simulation-Based Inference for Astronomical Applications

Many complex datasets in modern astronomy and cosmology are the result of intricate physical phenomena combined with detailed instrumental effects and observational processes. In these cases, accurately specifying the probability distribution of the data given the parameters may be difficult or impossible, making statistical inference using conventional likelihood-based (including Bayesian) methods intractable. A introduction will be given to motivate machine learning-enabled methods for statistical inference broadly applicable when a forward model encapsulating the complex physical, instrumental, and observational effects can be used to simulate realistic data. Such simulation-based inference methods promise to enable effective statistical inference for many previously intractable data analysis problems in astronomy.

astrostatistics

Bayesian methods

machine learning

simulation-based inference