Stan
Stan: A C++ Library for Probability and Sampling. Stan is a probabilistic programming language implementing full Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized maximum likelihood estimation with Optimization (BFGS). Stan is coded in C++ and runs on all major platforms.
Keywords for this software
References in zbMATH (referenced in 44 articles )
Showing results 1 to 20 of 44.
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- Fasiolo, Matteo; de Melo, Flávio Eler; Maskell, Simon: Langevin incremental mixture importance sampling (2018)
- Selland Kleppe, Tore: Modified Cholesky Riemann manifold Hamiltonian Monte Carlo: exploiting sparsity for fast sampling of high-dimensional targets (2018)
- Wang, Xiaofeng; Yue, Yu Ryan; Faraway, Julian J.: Bayesian regression modeling with INLA (2018)
- Bob Carpenter and Andrew Gelman and Matthew Hoffman and Daniel Lee and Ben Goodrich and Michael Betancourt and Marcus Brubaker and Jiqiang Guo and Peter Li and Allen Riddell: Stan: A Probabilistic Programming Language (2017)
- Corani, Giorgio; Benavoli, Alessio; Demšar, Janez; Mangili, Francesca; Zaffalon, Marco: Statistical comparison of classifiers through Bayesian hierarchical modelling (2017)
- Culpepper, Ryan; Cobb, Andrew: Contextual equivalence for probabilistic programs with continuous random variables and scoring (2017)
- Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
- Geppert, Leo N.; Ickstadt, Katja; Munteanu, Alexander; Quedenfeld, Jens; Sohler, Christian: Random projections for Bayesian regression (2017)
- Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.: Bayesian models for astrophysical data. Using R, JAGS, Python, and Stan (2017)
- Houpt, Joseph W.; Fifić, Mario: A hierarchical Bayesian approach to distinguishing serial and parallel processing (2017)
- Kühnel, Line; Sommer, Stefan; Pai, Akshay; Raket, Lars Lau: Most likely separation of intensity and warping effects in image registration (2017)
- Lathrop, Quinn N.; Cheng, Ying: Item cloning variation and the impact on the parameters of response models (2017)
- Liu, Yang; Hannig, Jan: Generalized fiducial inference for logistic graded response models (2017)
- Paul-Christian Buerkner: Bayesian Distributional Non-Linear Multilevel Modeling with the R Package brms (2017) arXiv
- Piironen, Juho; Vehtari, Aki: Comparison of Bayesian predictive methods for model selection (2017)
- Quentin F. Gronau, Henrik Singmann, Eric-Jan Wagenmakers: bridgesampling: An R Package for Estimating Normalizing Constants (2017) arXiv
- Staton, Sam: Commutative semantics for probabilistic programming (2017)
- Vehtari, Aki; Gelman, Andrew; Gabry, Jonah: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (2017)
- Fullerton, Andrew S.; Xu, Jun: Ordered regression models. Parallel, partial, and non-parallel alternatives (2016)
- Houpt, Joseph W.; MacEachern, Steven N.; Peruggia, Mario; Townsend, James T.; Van Zandt, Trisha: Semiparametric Bayesian approaches to systems factorial technology (2016)