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 102 articles , 1 standard article )

Showing results 1 to 20 of 102.
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  1. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  2. Antonio Calcagnì, Massimiliano Pastore, Gianmarco Altoè: ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in R (2019) arXiv
  3. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  4. 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
  5. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  6. Gelling, Nicholas; Schofield, Matthew R.; Barker, Richard J.: \textsfRpackage \textsfrjmcmc: reversible jump MCMC using post-processing (2019)
  7. George G Vega Yon; Paul Marjoram: fmcmc: A friendly MCMC framework (2019) not zbMATH
  8. Jure Demšar, Grega Repovš, Erik Štrumbelj: bayes4psy - an Open Source R Package for Bayesian Statistics in Psychology (2019) arXiv
  9. Ng, Kenyon; Turlach, Berwin A.; Murray, Kevin: A flexible sequential Monte Carlo algorithm for parametric constrained regression (2019)
  10. Paul-Christian Burkner: thurstonianIRT: Thurstonian IRT Models in R (2019) not zbMATH
  11. Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
  12. Adam Peterson, Brisa Sanchez: rstap: An R Package for Spatial Temporal Aggregated Predictor Models (2018) arXiv
  13. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  14. Boehm, Udo; Annis, Jeffrey; Frank, Michael J.; Hawkins, Guy E.; Heathcote, Andrew; Kellen, David; Krypotos, Angelos-Miltiadis; Lerche, Veronika; Logan, Gordon D.; Palmeri, Thomas J.; van Ravenzwaaij, Don; Servant, Mathieu; Singmann, Henrik; Starns, Jeffrey J.; Voss, Andreas; Wiecki, Thomas V.; Matzke, Dora; Wagenmakers, Eric-Jan: Estimating across-trial variability parameters of the diffusion decision model: expert advice and recommendations (2018)
  15. Brendon Brewer; Daniel Foreman-Mackey: DNest4: Diffusive Nested Sampling in C++ and Python (2018) not zbMATH
  16. Casey Youngflesh: MCMCvis: Tools to Visualize, Manipulate, and Summarize MCMC Output (2018) not zbMATH
  17. Depaoli, Sarah; Liu, Yang: Book review of: R. Levy and R. J. Mislevy, Bayesian psychometric modeling (2018)
  18. Ding, Peng; Li, Fan: Causal inference: a missing data perspective (2018)
  19. Fasiolo, Matteo; de Melo, Flávio Eler; Maskell, Simon: Langevin incremental mixture importance sampling (2018)
  20. Faulkner, James R.; Minin, Vladimir N.: Locally adaptive smoothing with Markov random fields and shrinkage priors (2018)

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