ma921/BASQ
(NeurIPS 2022) Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
This project helps machine learning practitioners or statisticians quickly and accurately estimate complex probability distributions in Bayesian models. It takes in information about your model's likelihood and prior distributions, and efficiently produces a reliable approximation of the posterior distribution. This is ideal for those working on advanced probabilistic modeling and inference tasks.
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Use this if you need to perform fast and diverse sampling to approximate complex posterior distributions in high-dimensional Bayesian inference problems.
Not ideal if you are looking for a general-purpose, easy-to-use Bayesian inference library without requiring advanced knowledge of Bayesian quadrature techniques.
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Language
Python
License
BSD-3-Clause
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Last pushed
Nov 16, 2023
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