approxposterior¶
approxposterior
is a Python package for efficient approximate Bayesian
inference and Bayesian optimization of computationally-expensive models. approxposterior
trains a Gaussian process (GP) surrogate model for the computationally-expensive model
and employs an active learning approach to iteratively improve the GPs predictive
performance while minimizing the number of calls to the expensive model required
to generate the GP’s training set.
approxposterior
implements variants of Bayesian Active Learning for Posterior Estimation (BAPE)
by Kandasamy et al. (2017) and Adaptive Gaussian process approximation for
Bayesian inference with expensive likelihood functions (AGP) by Wang & Li (2018).
These active learning algorithms outline schemes for GP active learning that approxposterior
uses for its Bayesian posterior inference. approxposterior
implemented the Jones et al. (1998)
Expected Utility function for Bayesian optimization.
For information on how to install approxposterior
, numerous examples, and detailed API
documentation, check out the Table of Contents to the left and below.