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.


References in zbMATH (referenced in 286 articles , 1 standard article )

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  1. 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)
  2. 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
  3. Faridi, Masoud; Khaledi, Majid Jafari: The polar-generalized normal distribution: properties, Bayesian estimation and applications (2022)
  4. Fisher, Christopher R.; Houpt, Joseph W.; Gunzelmann, Glenn: Fundamental tools for developing likelihood functions within ACT-R (2022)
  5. Manderson, Andrew A.; Goudie, Robert J. B.: A numerically stable algorithm for integrating Bayesian models using Markov melding (2022)
  6. 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)
  7. Papastamoulis, Panagiotis; Ntzoufras, Ioannis: On the identifiability of Bayesian factor analytic models (2022)
  8. 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)
  9. 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)
  10. Vono, Maxime; Dobigeon, Nicolas; Chainais, Pierre: High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm (2022)
  11. Wheatley, David; Bayley, Tiffany; Araghi, Mojtaba: Able construction: a spreadsheet activity for teaching Bayes’ theorem (2022)
  12. Yamaguchi, Kazuhiro; Templin, Jonathan: A Gibbs sampling algorithm with monotonicity constraints for diagnostic classification models (2022)
  13. Ye, Keying; Han, Zifei; Duan, Yuyan; Bai, Tianyu: Normalized power prior Bayesian analysis (2022)
  14. Zhou, Haiming; Huang, Xianzheng: Bayesian beta regression for bounded responses with unknown supports (2022)
  15. Asar, Özgür: Bayesian analysis of Turkish income and living conditions data, using clustered longitudinal ordinal modelling with bridge distributed random effects (2021)
  16. Biswas, Aniket; Chakraborty, Subrata; Mukherjee, Meghna: On estimation of stress-strength reliability with log-Lindley distribution (2021)
  17. 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
  18. Bürkner, Paul-Christian; Gabry, Jonah; Vehtari, Aki: Efficient leave-one-out cross-validation for Bayesian non-factorized normal and student-(t) models (2021)
  19. 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)
  20. Cordoba, Karen Rosana; Montenegro, Alvaro Mauricio: Bayesian multi-faceted TRI models for measuring professor’s performance in the classroom (2021)

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