BayesPy: Variational Bayesian Inference in Python

원문 초록

BayesPy is an open-source Python software package for performing variational Bayesian inference. It is based on the variational message passing framework and supports conjugate exponential family models. By removing the tedious task of implementing the variational Bayesian update equations, the user can construct models faster and in a less error-prone way. Simple syntax, flexible model construction and efficient inference make BayesPy suitable for both average and expert Bayesian users. It also supports some advanced methods such as stochastic and collapsed variational inference.

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arXiv:2506.21408공통 태그 Bayesian Inference, Variational Inference공식 코드 · Papers with Code 등록 2025-06-26

Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference

arXiv:2506.13613공통 태그 Bayesian Inference, Variational Inference공식 코드 · Papers with Code 등록 2025-06-16

Variational Inference with Mixtures of Isotropic Gaussians

arXiv:2506.04749공통 태그 Bayesian Inference, Variational Inference공식 코드 · Papers with Code 등록 2025-06-05

Amortized variational transdimensional inference

arXiv:2503.11808공통 태그 Bayesian Inference, Variational Inference공식 코드 · Papers with Code 등록 2025-03-14

Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural Networks

arXiv:2501.16825공통 태그 Bayesian Inference, Variational Inference공식 코드 · Papers with Code 등록 2025-01-28

Can Transformers Learn Full Bayesian Inference in Context?

arXiv:2501.04259공통 태그 Bayesian Inference, Variational Inference공식 코드 · Papers with Code 등록 2025-01-08

Stable Derivative Free Gaussian Mixture Variational Inference for Bayesian Inverse Problems

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