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

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References in zbMATH (referenced in 22 articles )

Showing results 1 to 20 of 22.
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  1. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  2. Geppert, Leo N.; Ickstadt, Katja; Munteanu, Alexander; Quedenfeld, Jens; Sohler, Christian: Random projections for Bayesian regression (2017)
  3. Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.: Bayesian models for astrophysical data. Using R, JAGS, Python, and Stan (2017)
  4. Kühnel, Line; Sommer, Stefan; Pai, Akshay; Raket, Lars Lau: Most likely separation of intensity and warping effects in image registration (2017)
  5. Lathrop, Quinn N.; Cheng, Ying: Item cloning variation and the impact on the parameters of response models (2017)
  6. Paul-Christian Buerkner: Bayesian Distributional Non-Linear Multilevel Modeling with the R Package brms (2017) arXiv
  7. Piironen, Juho; Vehtari, Aki: Comparison of Bayesian predictive methods for model selection (2017)
  8. Vehtari, Aki; Gelman, Andrew; Gabry, Jonah: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (2017)
  9. Fullerton, Andrew S.; Xu, Jun: Ordered regression models. Parallel, partial, and non-parallel alternatives (2016)
  10. Houpt, Joseph W.; MacEachern, Steven N.; Peruggia, Mario; Townsend, James T.; Van Zandt, Trisha: Semiparametric Bayesian approaches to systems factorial technology (2016)
  11. Katahira, Kentaro: How hierarchical models improve point estimates of model parameters at the individual level (2016)
  12. Luttinen, Jaakko: BayesPy: variational Bayesian inference in Python (2016)
  13. Philipp H Boersch-Supan, Leah R Johnson: deBInfer: Bayesian inference for dynamical models of biological systems in R (2016) arXiv
  14. Shiffrin, Richard M.; Chandramouli, Suyog H.; Grünwald, Peter D.: Bayes factors, relations to minimum description length, and overlapping model classes (2016)
  15. Green, Peter J.; Łatuszyński, Krzysztof; Pereyra, Marcelo; Robert, Christian P.: Bayesian computation: a summary of the current state, and samples backwards and forwards (2015)
  16. Niemi, Jarad; Mittman, Eric; Landau, Will; Nettleton, Dan: Empirical Bayes analysis of RNA-seq data for detection of gene expression heterosis (2015)
  17. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2015)
  18. Vincent, Benjamin T.: A tutorial on Bayesian models of perception (2015)
  19. Adrien Todeschini, Francois Caron, Marc Fuentes, Pierrick Legrand, Pierre Del Moral: Biips: Software for Bayesian Inference with Interacting Particle Systems (2014) arXiv
  20. Kruschke, John: Doing Bayesian data analysis. A tutorial introduction with R, JAGS, and Stan (2014)

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