BayesPy: Variational Bayesian Inference in Python

10/03/2014
by   Jaakko Luttinen, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/10/2018

Bayesian variational inference for exponential random graph models

Bayesian inference for exponential random graphs (ERGMs) is a doubly int...
research
12/25/2021

Reactive Message Passing for Scalable Bayesian Inference

We introduce Reactive Message Passing (RMP) as a framework for executing...
research
05/23/2018

Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers

We present a scalable approach to performing approximate fully Bayesian ...
research
10/18/2018

Good Initializations of Variational Bayes for Deep Models

Stochastic variational inference is an established way to carry out appr...
research
10/25/2017

General Bayesian Inference over the Stiefel Manifold via the Givens Transform

We introduce the Givens Transform, a novel transform between the space o...
research
02/23/2015

Scalable Variational Inference in Log-supermodular Models

We consider the problem of approximate Bayesian inference in log-supermo...
research
10/31/2012

Linear-Nonlinear-Poisson Neuron Networks Perform Bayesian Inference On Boltzmann Machines

One conjecture in both deep learning and classical connectionist viewpoi...

Please sign up or login with your details

Forgot password? Click here to reset