JAGS

JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: (1) To have a cross-platform engine for the BUGS language. (2) To be extensible, allowing users to write their own functions, distributions and samplers. (3) To be a plaftorm for experimentation with ideas in Bayesian modelling. JAGS is licensed under the GNU General Public License. You may freely modify and redistribute it under certain conditions (see the file COPYING for details).


References in zbMATH (referenced in 190 articles )

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  1. Albert, Jim; Hu, Jingchen: Probability and Bayesian modeling (2020)
  2. de Castro, Mário; Gómez, Yolanda M.: A Bayesian cure rate model based on the power piecewise exponential distribution (2020)
  3. Ferreira, Paulo H.; Ramos, Eduardo; Ramos, Pedro L.; Gonzales, Jhon F. B.; Tomazella, Vera L. D.; Ehlers, Ricardo S.; Silva, Eveliny B.; Louzada, Francisco: Objective Bayesian analysis for the Lomax distribution (2020)
  4. Gianluca Baio: survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling (2020) not zbMATH
  5. Lee, Michael D.; Bock, Jason R.; Cushman, Isaiah; Shankle, William R.: An application of multinomial processing tree models and Bayesian methods to understanding memory impairment (2020)
  6. Miller, David L.; Glennie, Richard; Seaton, Andrew E.: Understanding the stochastic partial differential equation approach to smoothing (2020)
  7. Oh, Rosy; Shi, Peng; Ahn, Jae Youn: Bonus-malus premiums under the dependent frequency-severity modeling (2020)
  8. Osthus, Dave; Hyman, Jeffrey D.; Karra, Satish; Panda, Nishant; Srinivasan, Gowri: A probabilistic clustering approach for identifying primary subnetworks of discrete fracture networks with quantified uncertainty (2020)
  9. Robert J. B. Goudie, Rebecca M. Turner, Daniela De Angelis, Andrew Thomas: MultiBUGS: A Parallel Implementation of the BUGS Modeling Framework for Faster Bayesian Inference (2020) not zbMATH
  10. Storlie, Curtis B.; Therneau, Terry M.; Carter, Rickey E.; Chia, Nicholas; Bergquist, John R.; Huddleston, Jeanne M.; Romero-Brufau, Santiago: Prediction and inference with missing data in patient alert systems (2020)
  11. Timothy D. Meehan, Nicole L. Michel, Håvard Rue: Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R (2020) not zbMATH
  12. Wood, Simon N.: Inference and computation with generalized additive models and their extensions (2020)
  13. Zhan, Peida; Wang, Wen-Chung; Li, Xiaomin: A partial mastery, higher-order latent structural model for polytomous attributes in cognitive diagnostic assessments (2020)
  14. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  15. Amoros, Ruben; King, Ruth; Toyoda, Hidenori; Kumada, Takashi; Johnson, Philip J.; Bird, Thomas G.: A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular carcinoma (2019)
  16. Arellano-Valle, Reinaldo B.; Contreras-Reyes, Javier E.; Quintero, Freddy O. López; Valdebenito, Abel: A skew-normal dynamic linear model and Bayesian forecasting (2019)
  17. Cox, Gregory E.; Criss, Amy H.: Parametric supplements to systems factorial analysis: identifying interactive parallel processing using systems of accumulators (2019)
  18. Daniel Sabanés Bové, Wai Yin Yeung, Giuseppe Palermo, Thomas Jaki: Model-Based Dose Escalation Designs in R with crmPack (2019) not zbMATH
  19. Finke, Axel; King, Ruth; Beskos, Alexandros; Dellaportas, Petros: Efficient sequential Monte Carlo algorithms for integrated population models (2019)
  20. Gelling, Nicholas; Schofield, Matthew R.; Barker, Richard J.: \textsfRpackage \textsfrjmcmc: reversible jump MCMC using post-processing (2019)

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