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dynesty is a Dynamic Nested Sampling package for estimating Bayesian posteriors and evidences. dynesty samples from a given distribution when provided with a loglikelihood function, a prior_transform function (that transforms samples from the unit cube to the target prior), and the dimensionality of the parameter space.
Prospector conducts principled inference of stellar population properties from photometric and/or spectroscopic data. The code combine photometric and spectroscopic data rigorously using a flexible spectroscopic calibration model and infer high-dimensional stellar population properties using parameteric SFHs (with ensemble MCMC sampling). Prospector also constrains the linear combination of stellar population components that are present in a galaxy (e.g. non-parametric SFHs) using spectra and/or photometry, and fits individual stellar spectra using large interpolated grids.
SBI++ is a complete methodology based on simulation-based (likelihood-free) inference that is customized for astronomical applications. Specifically, the code retains the fast inference speed of ∼1 sec for objects in the observational training set distribution, and additionally permits parameter inference outside of the trained noise and data at ~1 min per object. The package includes scripts for training and implementing SBI++ and is dependent on sbi (ascl:2306.002).