scholarly journals sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo

2019 ◽  
Vol 91 (3) ◽  
Author(s):  
Jack Baker ◽  
Paul Fearnhead ◽  
Emily B. Fox ◽  
Christopher Nemeth
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
R Package ◽  
Stochastic Gradient

scholarly journals bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'

2021 ◽  
Author(s):  
AISDL
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Networks ◽  
R Package ◽  
Open Science ◽  
Associated Functions ◽  
Science Framework ◽  
Graphical Structure

Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate 'Stan' code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) The 'bayesvl' R package. Open Science Framework (May 18).

scholarly journals Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection

Biometrika ◽  
2020 ◽  
Vol 107 (4) ◽  
pp. 997-1004
Author(s):  
Qifan Song ◽  
Yan Sun ◽  
Mao Ye ◽  
Faming Liang
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Latent Variables ◽  
Large Scale ◽  
Bayesian Variable Selection ◽  
Stochastic Gradient ◽  
Monte Carlo Algorithm ◽  
Monte Carlo Algorithms ◽  
Fixed Dimension

Summary Stochastic gradient Markov chain Monte Carlo algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters. This paper proposes an extended stochastic gradient Markov chain Monte Carlo algorithm which, by introducing appropriate latent variables, can be applied to more general large-scale Bayesian computing problems, such as those involving dimension jumping and missing data. Numerical studies show that the proposed algorithm is highly scalable and much more efficient than traditional Markov chain Monte Carlo algorithms.

scholarly journals Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo

2021 ◽  
Vol 43 (1) ◽  
pp. A26-A53
Author(s):  
Bao Wang ◽  
Difan Zou ◽  
Quanquan Gu ◽  
Stanley J. Osher
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Stochastic Gradient ◽  
Laplacian Smoothing

scholarly journals bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'

2020 ◽  
Author(s):  
AISDL
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Networks ◽  
R Package ◽  
Open Science ◽  
Associated Functions ◽  
Science Framework ◽  
Graphical Structure

Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate 'Stan' code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) The 'bayesvl' R package. Open Science Framework (May 18).Version: 0.8.5Depends: R (≥ 3.4.0), rstan (≥ 2.10.0), StanHeaders (≥ 2.18.0), stats, graphics, methodsImports: coda, bnlearn, ggplot2, bayesplot, viridis, reshape2, dplyrSuggests: loo (≥ 2.0.0)Published: 2019-05-24Author: Viet-Phuong La [aut, cre], Quan-Hoang Vuong [aut]Maintainer: Viet-Phuong La BugReports: https://github.com/sshpa/bayesvl/issuesLicense: GPL (≥ 3)URL: https://github.com/sshpa/bayesvl

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