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 286 articles , 1 standard article )
Showing results 1 to 20 of 286.
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- Cabral, Celso Rômulo Barbosa; de Souza, Nelson Lima; Leão, Jeremias: Bayesian measurement error models using finite mixtures of scale mixtures of skew-normal distributions (2022)
- Edgar Santos-Fernandez, Jay M. Ver Hoef, James M. McGree, Daniel J. Isaak, Kerrie Mengersen, Erin E. Peterson: SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks (2022) arXiv
- Faridi, Masoud; Khaledi, Majid Jafari: The polar-generalized normal distribution: properties, Bayesian estimation and applications (2022)
- Fisher, Christopher R.; Houpt, Joseph W.; Gunzelmann, Glenn: Fundamental tools for developing likelihood functions within ACT-R (2022)
- Manderson, Andrew A.; Goudie, Robert J. B.: A numerically stable algorithm for integrating Bayesian models using Markov melding (2022)
- Martin, Sergio M.; Wälchli, Daniel; Arampatzis, Georgios; Economides, Athena E.; Karnakov, Petr; Koumoutsakos, Petros: Korali: efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization (2022)
- Papastamoulis, Panagiotis; Ntzoufras, Ioannis: On the identifiability of Bayesian factor analytic models (2022)
- Pelle, Elvira; Zaccarin, Susanna; Furfaro, Emanuela; Rivellini, Giulia: Support provided by elderly in Italy: a hierarchical analysis of ego networks controlling for alter-overlapping (2022)
- Santos-Fernandez, Edgar; Ver Hoef, Jay M.; Peterson, Erin E.; McGree, James; Isaak, Daniel J.; Mengersen, Kerrie: Bayesian spatio-temporal models for stream networks (2022)
- Vono, Maxime; Dobigeon, Nicolas; Chainais, Pierre: High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm (2022)
- Wheatley, David; Bayley, Tiffany; Araghi, Mojtaba: Able construction: a spreadsheet activity for teaching Bayes’ theorem (2022)
- Yamaguchi, Kazuhiro; Templin, Jonathan: A Gibbs sampling algorithm with monotonicity constraints for diagnostic classification models (2022)
- Ye, Keying; Han, Zifei; Duan, Yuyan; Bai, Tianyu: Normalized power prior Bayesian analysis (2022)
- Zhou, Haiming; Huang, Xianzheng: Bayesian beta regression for bounded responses with unknown supports (2022)
- Asar, Özgür: Bayesian analysis of Turkish income and living conditions data, using clustered longitudinal ordinal modelling with bridge distributed random effects (2021)
- Biswas, Aniket; Chakraborty, Subrata; Mukherjee, Meghna: On estimation of stress-strength reliability with log-Lindley distribution (2021)
- Brandon P.M. Edwards, Adam C. Smith: bbsBayes: An R Package for Hierarchical Bayesian Analysis of North American Breeding Bird Survey Data (2021) not zbMATH
- Bürkner, Paul-Christian; Gabry, Jonah; Vehtari, Aki: Efficient leave-one-out cross-validation for Bayesian non-factorized normal and student-(t) models (2021)
- Castillo-Laborde, Carla; de Wolff, Taco; Gajardo, Pedro; Lecaros, Rodrigo; Olivar-Tost, Gerard; Ramírez C., Héctor: Assessment of event-triggered policies of nonpharmaceutical interventions based on epidemiological indicators (2021)
- Cordoba, Karen Rosana; Montenegro, Alvaro Mauricio: Bayesian multi-faceted TRI models for measuring professor’s performance in the classroom (2021)