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.Peer reviewe
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesi...
Variational Bayesian (VB) inference has become an increasingly popular method for approximating exac...
BayesPy is an open-source Python software package for performing variational Bayesian inference. It ...
ABCpy is a highly modular, scientific library for Approximate Bayesian Computation (ABC) written in ...
BayesicFitting is a PYTHON toolbox for (Bayesian) data modelling and evidence calculation. It consis...
ABCpy is a highly modular scientific library for approximate Bayesian computation (ABC) written in P...
The popularity of Bayesian statistical methods has increased dramatically in recent years across man...
This repository includes all the scripts used for sampling in the manuscript entitled "Posterior mar...
BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlin-ear optimi...
This paper presents Variational Message Passing (VMP), a general purpose algorithm for applying vari...
Bayesian inference is a widely used and powerful analytical technique in fields such as astronomy an...
Variational Message Passing (VMP) provides an automatable and efficient algorithmic framework for ap...
Motivation: The growing field of systems biology has driven demand for flexible tools to model and s...
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions o...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesi...
Variational Bayesian (VB) inference has become an increasingly popular method for approximating exac...
BayesPy is an open-source Python software package for performing variational Bayesian inference. It ...
ABCpy is a highly modular, scientific library for Approximate Bayesian Computation (ABC) written in ...
BayesicFitting is a PYTHON toolbox for (Bayesian) data modelling and evidence calculation. It consis...
ABCpy is a highly modular scientific library for approximate Bayesian computation (ABC) written in P...
The popularity of Bayesian statistical methods has increased dramatically in recent years across man...
This repository includes all the scripts used for sampling in the manuscript entitled "Posterior mar...
BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlin-ear optimi...
This paper presents Variational Message Passing (VMP), a general purpose algorithm for applying vari...
Bayesian inference is a widely used and powerful analytical technique in fields such as astronomy an...
Variational Message Passing (VMP) provides an automatable and efficient algorithmic framework for ap...
Motivation: The growing field of systems biology has driven demand for flexible tools to model and s...
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions o...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesi...
Variational Bayesian (VB) inference has become an increasingly popular method for approximating exac...