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 200 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. Jobst, Lisa J.; Heck, Daniel W.; Moshagen, Morten: A comparison of correlation and regression approaches for multinomial processing tree models (2020)
  6. 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)
  7. Ma, Zhihua; Chen, Guanghui: Bayesian semiparametric latent variable model with DP prior for joint analysis: implementation with nimble (2020)
  8. Merkle, Edgar C.; Saw, Geoff; Davis-Stober, Clintin: Beating the average forecast: regularization based on forecaster attributes (2020)
  9. Michalkiewicz, Martha; Horn, Sebastian S.; Bayen, Ute J.: Hierarchical multinomial modeling to explain individual differences in children’s clustering in free recall (2020)
  10. Miller, David L.; Glennie, Richard; Seaton, Andrew E.: Understanding the stochastic partial differential equation approach to smoothing (2020)
  11. Oh, Rosy; Shi, Peng; Ahn, Jae Youn: Bonus-malus premiums under the dependent frequency-severity modeling (2020)
  12. Oravecz, Zita; Vandekerckhove, Joachim: A joint process model of consensus and longitudinal dynamics (2020)
  13. 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)
  14. 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
  15. 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)
  16. 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
  17. Wood, Simon N.: Inference and computation with generalized additive models and their extensions (2020)
  18. Zhan, Peida; Wang, Wen-Chung; Li, Xiaomin: A partial mastery, higher-order latent structural model for polytomous attributes in cognitive diagnostic assessments (2020)
  19. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  20. 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)

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