<i>Stan</i> : A Probabilistic Programming Language
Pourquoi ce travail est-il dans la base ?
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Résumé
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 cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
La notice
- Revue
- Journal of Statistical Software
- Thématique
- Statistical Methods and Bayesian Inference
- Domaine
- Mathematics
- Établissements canadiens
- York University
- Organismes subventionnaires
- National Center for Research ResourcesInstitute of Education SciencesU.S. Department of EnergyNational Science FoundationNational Institutes of HealthHarvard University
- Mots-clés
- Python (programming language)Computer scienceMarkov chain Monte CarloAlgorithmHybrid Monte CarloMonte Carlo methodProbabilistic logicBayesian inferenceImportance samplingStatistical inferenceInferenceMonte Carlo integrationApplied mathematicsBayesian probabilityMathematical optimizationMathematicsProgramming languageArtificial intelligenceStatistics
- Résumé présent dans OpenAlex
- oui