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 154 articles , 1 standard article )

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  1. Andrade, Daniel; Takeda, Akiko; Fukumizu, Kenji: Robust Bayesian model selection for variable clustering with the Gaussian graphical model (2020)
  2. Chenguang Wang, Elizabeth Colantuoni, Andrew Leroux, Daniel O. Scharfstein: idem: An R Package for Inferences in Clinical Trials with Death and Missingness (2020) not zbMATH
  3. Izhar Asael Alonzo Matamoros, Cristian Andres Cruz Torres: varstan: An R package for Bayesian analysis of structured time series models with Stan (2020) arXiv
  4. Jauch, Michael; Hoff, Peter D.; Dunson, David B.: Random orthogonal matrices and the Cayley transform (2020)
  5. Karimi, Belhal; Lavielle, Marc; Moulines, Eric: f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models (2020)
  6. Li, Yicheng; Raftery, Adrian E.: Estimating and forecasting the smoking-attributable mortality fraction for both genders jointly in over 60 countries (2020)
  7. Moores, Matthew; Nicholls, Geoff; Pettitt, Anthony; Mengersen, Kerrie: Scalable Bayesian inference for the inverse temperature of a hidden Potts model (2020)
  8. Mulder, Kees; Klugkist, Irene; van Renswoude, Daan; Visser, Ingmar: Mixtures of peaked power Batschelet distributions for circular data with application to saccade directions (2020)
  9. Panagiotis Papastamoulis, Ioannis Ntzoufras: On the identifiability of Bayesian factor analytic models (2020) arXiv
  10. Renato Valladares Panaro: spsurv: An R package for semi-parametric survival analysis (2020) arXiv
  11. Riko Kelter: fbst: An R package for the Full Bayesian Significance Test for testing a sharp null hypothesis against its alternative via the e-value (2020) arXiv
  12. Taysseer Sharaf; Theren Williams; Abdallah Chehade; Keshav Pokhrel: BLNN: An R package for training neural networks using Bayesian inference (2020) not zbMATH
  13. Thach, Tien T.; Bris, Radim; Volf, Petr; Coolen, Frank P. A.: Non-linear failure rate: a Bayes study using Hamiltonian Monte Carlo simulation (2020)
  14. Vanslette, Kevin; Al Alsheikh, Abdullatif; Youcef-Toumi, Kamal: Why simple quadrature is just as good as Monte Carlo (2020)
  15. Volodina, Victoria; Williamson, Daniel: Diagnostics-driven nonstationary emulators using kernel mixtures (2020)
  16. Williams, Matthew R.; Savitsky, Terrance D.: Bayesian estimation under informative sampling with unattenuated dependence (2020)
  17. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  18. Antonio Calcagnì, Massimiliano Pastore, Gianmarco Altoè: ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in R (2019) arXiv
  19. Azzimonti, Laura; Corani, Giorgio; Zaffalon, Marco: Hierarchical estimation of parameters in Bayesian networks (2019)
  20. Bingham, Eli; Chen, Jonathan P.; Jankowiak, Martin; Obermeyer, Fritz; Pradhan, Neeraj; Karaletsos, Theofanis; Singh, Rohit; Szerlip, Paul; Horsfall, Paul; Goodman, Noah D.: Pyro: deep universal probabilistic programming (2019)

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