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 209 articles , 1 standard article )

Showing results 1 to 20 of 209.
Sorted by year (citations)

1 2 3 ... 9 10 11 next

  1. Ye, Keying; Han, Zifei; Duan, Yuyan; Bai, Tianyu: Normalized power prior Bayesian analysis (2022)
  2. 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
  3. Bürkner, Paul-Christian; Gabry, Jonah; Vehtari, Aki: Efficient leave-one-out cross-validation for Bayesian non-factorized normal and student-(t) models (2021)
  4. 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)
  5. David Issa Mattos, Érika Martins Silva Ramos: Bayesian Paired-Comparison with the bpcs Package (2021) arXiv
  6. Demarqui, Fábio N.; Mayrink, Vinícius D.: Yang and Prentice model with piecewise exponential baseline distribution for modeling lifetime data with crossing survival curves (2021)
  7. Gagnon, Philippe: Informed reversible jump algorithms (2021)
  8. Hartmann, Raphael; Klauer, Karl Christoph: Partial derivatives for the first-passage time distribution in Wiener diffusion models (2021)
  9. Ibargüen-Mondragón, Eduardo; Prieto, Kernel; Hidalgo-Bonilla, Sandra Patricia: A model on bacterial resistance considering a generalized law of mass action for plasmid replication (2021)
  10. James Yang: FastAD: Expression Template-Based C++ Library for Fast and Memory-Efficient Automatic Differentiation (2021) arXiv
  11. John Taylor Chavis, Amy Louise Cochran, Christopher James Earls: CU-MSDSp: A flexible parallelized Reversible jump Markov chain Monte Carlo method (2021) not zbMATH
  12. Kelter, Riko: Bayesian model selection in the (\mathcalM)-open setting -- approximate posterior inference and subsampling for efficient large-scale leave-one-out cross-validation via the difference estimator (2021)
  13. Kelter, Riko: Analysis of type I and II error rates of Bayesian and frequentist parametric and nonparametric two-sample hypothesis tests under preliminary assessment of normality (2021)
  14. Lukas Prediger, Niki Loppi, Samuel Kaski, Antti Honkela: d3p - A Python Package for Differentially-Private Probabilistic Programming (2021) arXiv
  15. Mathieu Besançon, Theodore Papamarkou, David Anthoff, Alex Arslan, Simon Byrne, Dahua Lin, John Pearson: Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem (2021) not zbMATH
  16. Mauff, Katya; Erler, Nicole S.; Kardys, Isabella; Rizopoulos, Dimitris: Pairwise estimation of multivariate longitudinal outcomes in a Bayesian setting with extensions to the joint model (2021)
  17. Nemeth, Christopher; Fearnhead, Paul: Stochastic gradient Markov chain Monte Carlo (2021)
  18. Park, Harhim; Yang, Jaeyeong; Vassileva, Jasmin; Ahn, Woo-Young: Development of a novel computational model for the balloon analogue risk task: the exponential-weight mean-variance model (2021)
  19. Philippe Rast; Stephen Martin: bmgarch: An R-Package for Bayesian Multivariate GARCH models (2021) not zbMATH
  20. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)

1 2 3 ... 9 10 11 next