RStan: the R interface to Stan. rstan: User-facing R functions are provided by this package to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the ’StanHeaders’ package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Vehtari, Aki; Gelman, Andrew; Gabry, Jonah: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (2017)
- Katahira, Kentaro: How hierarchical models improve point estimates of model parameters at the individual level (2016)
- Okada, Kensuke; Lee, Michael D.: A Bayesian approach to modeling group and individual differences in multidimensional scaling (2016)
- Qin, Fei; Mai, Feng; Fry, Michael J.; Raturi, Amitabh S.: Supply-chain performance anomalies: fairness concerns under private cost information (2016)
- Scheipl, Fabian; Gertheiss, Jan; Greven, Sonja: Generalized functional additive mixed models (2016)
- Tan, Linda S.L.; Ong, Victor M.H.; Nott, David J.; Jasra, Ajay: Variational inference for sparse spectrum Gaussian process regression (2016)
- Liu, Ying; Gelman, Andrew; Zheng, Tian: Simulation-efficient shortest probability intervals (2015)
- Niemi, Jarad; Mittman, Eric; Landau, Will; Nettleton, Dan: Empirical Bayes analysis of RNA-seq data for detection of gene expression heterosis (2015)