- Referenced in 815 articles
- BayesDA: Functions and Datasets for the book ”Bayesian Data Analysis” Functions for Bayesian Data Analysis ... with datasets from the book ”Bayesian data Analysis (second edition)” by Gelman, Carlin, Stern...
- Referenced in 299 articles
- BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian...
- Referenced in 254 articles
- geostatistical data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves...
- Referenced in 146 articles
- tsbridge: Calculate normalising constants for Bayesian time series models. The tsbridge package contains a collection ... probabilities for a variety of time series Bayesian models, where parameters are estimated using BUGS...
- Referenced in 195 articles
- Based Clustering, Classification, and Density Estimation, including Bayesian regularization...
- Referenced in 136 articles
- program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation ... plaftorm for experimentation with ideas in Bayesian modelling. JAGS is licensed under the GNU General...
- Referenced in 102 articles
- probabilistic programming language implementing full Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized...
- Referenced in 68 articles
- Autoclass - A Bayesian Approach to Classification. We describe a Bayesian approach to the unsupervised discovery...
- Referenced in 91 articles
- framework encompassing machine learning, graphical models, and Bayesian statistics (hence the logo). (Some methods from...
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- BayesTree: Bayesian Methods for Tree Based Models: Implementation of BART: Bayesian Additive Regression Trees ... develop a Bayesian “sum-of-trees” model where each tree is constrained by a regularization ... inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from ... posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis...
- Referenced in 47 articles
- DPpackage: Bayesian Semi- and Nonparametric Modeling in R. Data analysis sometimes requires the relaxation ... specification of the probability model. In the Bayesian context, this is accomplished by placing ... programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently...
- Referenced in 53 articles
- PicHunter: Bayesian relevance feedback for image retrieval. This paper describes PicHunter, an image retrieval system ... goal image. To accomplish this, PicHunter uses Bayesian learning based on a probabilistic model...
- Referenced in 72 articles
- package boa: Bayesian Output Analysis Program (BOA) for MCMC. A menu-driven program and library...
- Referenced in 43 articles
- Teaching Bayesian statistics to marketing and business students. We discuss our experiences teaching Bayesian statistics ... course that emphasizes the value of the Bayesian approach to solving nontrivial problems. The success...
- Referenced in 62 articles
- BUGS is a software package for performing Bayesian inference Using Gibbs Sampling. The user specifies...
- Referenced in 62 articles
- bvarsv: Bayesian Analysis of a Vector Autoregressive Model with Stochastic Volatility and Time-Varying Parameters...
- Referenced in 59 articles
- HdBCS - High-dimensional Bayesian Covariance Selection. This site provides C++ code software implementing...
- Referenced in 56 articles
- easily compare, confirm and refine models. Uses Bayesian analysis—to improve estimates of model parameters...
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- package bnlearn: Bayesian network structure learning, parameter learning and inference. Bayesian network structure learning, parameter ... support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries and cross...
- Referenced in 26 articles
- package dlm: Bayesian and Likelihood Analysis of Dynamic Linear Models. Maximum likelihood, Kalman filtering ... smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear ... gives an introduction, presenting basic notions in Bayesian inference. The basic elements of Bayesian analysis ... much more elaborated one on Bayesian inference. The last chapter is on sequential Monte Carlo...