Stan : A Probabilistic Programming Language

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models throu...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of statistical software Ročník 76; číslo 1; s. 1 - 32
Hlavní autori: Carpenter, Bob, Gelman, Andrew, Hoffman, Matthew D., Lee, Daniel, Goodrich, Ben, Betancourt, Michael, Brubaker, Marcus, Guo, Jiqiang, Li, Peter, Riddell, Allen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Foundation for Open Access Statistics 2017
Predmet:
algorithmic differentiation
Bayesian inference
MATHEMATICS AND COMPUTING
probabilistic program
probabilistic programming
Stan
Stan
algorithmic differentiation
Bayesian inference
probabilistic program
ISSN:1548-7660, 1548-7660
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the package, through R using the package, and through Python using the package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. and also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
USDOE
SC0002099; ATM-0934516; ED-GRANTS-032309-005; R305D090006-09A; 1G20RR030893-01; CNS-1205516
National Science Foundation (NSF)
US Dept. of Education, Inst. of Education Sciences (IES)
National Institutes of Health (NIH)
ISSN:1548-7660
1548-7660
DOI:10.18637/jss.v076.i01