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 136 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. Jauch, Michael; Hoff, Peter D.; Dunson, David B.: Random orthogonal matrices and the Cayley transform (2020)
  3. Karimi, Belhal; Lavielle, Marc; Moulines, Eric: f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models (2020)
  4. Moores, Matthew; Nicholls, Geoff; Pettitt, Anthony; Mengersen, Kerrie: Scalable Bayesian inference for the inverse temperature of a hidden Potts model (2020)
  5. Mulder, Kees; Klugkist, Irene; van Renswoude, Daan; Visser, Ingmar: Mixtures of peaked power Batschelet distributions for circular data with application to saccade directions (2020)
  6. Renato Valladares Panaro: spsurv: An R package for semi-parametric survival analysis (2020) arXiv
  7. Taysseer Sharaf; Theren Williams; Abdallah Chehade; Keshav Pokhrel: BLNN: An R package for training neural networks using Bayesian inference (2020) not zbMATH
  8. Volodina, Victoria; Williamson, Daniel: Diagnostics-driven nonstationary emulators using kernel mixtures (2020)
  9. Williams, Matthew R.; Savitsky, Terrance D.: Bayesian estimation under informative sampling with unattenuated dependence (2020)
  10. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  11. Antonio Calcagnì, Massimiliano Pastore, Gianmarco Altoè: ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in R (2019) arXiv
  12. Azzimonti, Laura; Corani, Giorgio; Zaffalon, Marco: Hierarchical estimation of parameters in Bayesian networks (2019)
  13. 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)
  14. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  15. Branson, Zach; Rischard, Maxime; Bornn, Luke; Miratrix, Luke W.: A nonparametric Bayesian methodology for regression discontinuity designs (2019)
  16. Clerx, M., Robinson, M., Lambert, B., Lei, C.L., Ghosh, S., Mirams, G.R. and Gavaghan, D.J.: Probabilistic Inference on Noisy Time Series (PINTS) (2019) not zbMATH
  17. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  18. Cozman, Fabio Gagliardi; Mauá, Denis Deratani: The finite model theory of Bayesian network specifications: descriptive complexity and zero/one laws (2019)
  19. El-Bachir, Yousra; Davison, Anthony C.: Fast automatic smoothing for generalized additive models (2019)
  20. Finke, Axel; King, Ruth; Beskos, Alexandros; Dellaportas, Petros: Efficient sequential Monte Carlo algorithms for integrated population models (2019)

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