
BUGS
 Referenced in 336 articles
[sw07885]
 BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian...

Stan
 Referenced in 159 articles
[sw10200]
 probabilistic programming language implementing full Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized...

OpenBUGS
 Referenced in 68 articles
[sw08316]
 software package for performing Bayesian inference Using Gibbs Sampling. The user specifies a statistical model...

bnlearn
 Referenced in 50 articles
[sw08265]
 network structure learning, parameter learning and inference. Bayesian network structure learning, parameter learning and inference ... parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries and crossvalidation. Development...

BayesLogit
 Referenced in 44 articles
[sw09312]
 package BayesLogit: PolyaGamma Sampling. Bayesian inference for logistic models using PólyaGamma latent variables ... dataaugmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals...

MrBayes
 Referenced in 43 articles
[sw07715]
 MrBayes: Bayesian Inference of Phylogeny. MrBayes is a program for Bayesian inference and model choice...

RStan
 Referenced in 42 articles
[sw13990]
 probabilistic programming language that implements full Bayesian statistical inference ... Markov Chain Monte Carlo, rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood...

Mcmcpack
 Referenced in 48 articles
[sw07974]
 This package contains functions to perform Bayesian inference using posterior simulation for a number...

MultiNest
 Referenced in 33 articles
[sw10481]
 MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics. We present ... nested sampling algorithm, called MultiNest. This Bayesian inference tool calculates the evidence, with an associated...

dlm
 Referenced in 28 articles
[sw04503]
 Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models ... have developed an R package, DIM, for inference and forecasting with dynamic linear models ... introduction, presenting basic notions in Bayesian inference. The basic elements of Bayesian analysis for linear ... much more elaborated one on Bayesian inference. The last chapter is on sequential Monte Carlo...

BioBayes
 Referenced in 24 articles
[sw08082]
 BioBayes: a software package for Bayesian inference in systems biology. MOTIVATION: There are several levels ... observed directly. The methods of Bayesian inference provide a consistent framework for modelling and predicting ... present a software package for applying the Bayesian inferential methodology to problems in systems biology...

Infer.NET
 Referenced in 27 articles
[sw07886]
 algorithms and statistical routines for performing Bayesian inference. It has applications in a wide variety...

rstan
 Referenced in 19 articles
[sw16103]
 probabilistic programming language that implements full Bayesian statistical inference ... Markov Chain Monte Carlo, rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood...

ADVI
 Referenced in 20 articles
[sw34040]
 scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious modelspecific calculations ... automatic differentiation variational inference (ADVI). The user only provides a Bayesian model and a dataset ... images. With ADVI we can use variational inference on any model we write in Stan...

LaplacesDemon
 Referenced in 14 articles
[sw11391]
 package LaplacesDemon: Complete Environment for Bayesian Inference. Provides a complete environment for Bayesian inference using...

BayesTree
 Referenced in 57 articles
[sw07995]
 Bayesian Additive Regression Trees. We develop a Bayesian “sumoftrees” model where each tree ... learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that ... posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis ... likelihood. This approach enables full posterior inference including point and interval estimates of the unknown...

bfa
 Referenced in 20 articles
[sw07430]
 justifications for using this likelihood in Bayesian inference. We propose new default priors...

PILCO
 Referenced in 19 articles
[sw34813]
 modeling of the dynamics and approximate Bayesian inference for policy evaluation and improvement...

brms
 Referenced in 19 articles
[sw19099]
 multilevel models using Stan for full Bayesian inference. A wide range of distributions and link...

LibBi
 Referenced in 12 articles
[sw19384]
 package for statespace modelling and Bayesian inference on modern computer hardware, including multicore ... runs code for the given model, inference method and hardware platform. In presenting the software ... specialised methods developed for Bayesian inference with them. The focus is on sequential Monte Carlo...