<i>Stan</i> : A Probabilistic Programming Language
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Journal of Statistical Software
- Topic
- Statistical Methods and Bayesian Inference
- Field
- Mathematics
- Canadian institutions
- York University
- Funders
- National Center for Research ResourcesInstitute of Education SciencesU.S. Department of EnergyNational Science FoundationNational Institutes of HealthHarvard University
- Keywords
- Python (programming language)Computer scienceMarkov chain Monte CarloAlgorithmHybrid Monte CarloMonte Carlo methodProbabilistic logicBayesian inferenceImportance samplingStatistical inferenceInferenceMonte Carlo integrationApplied mathematicsBayesian probabilityMathematical optimizationMathematicsProgramming languageArtificial intelligenceStatistics
- Has abstract in OpenAlex
- yes