tsbridge

tsbridge: Calculate normalising constants for Bayesian time series models. The tsbridge package contains a collection of R functions that can be used to estimate normalising constants using the bridge sampler of Meng and Wong (1996). The functions can be applied to calculate posterior model probabilities for a variety of time series Bayesian models, where parameters are estimated using BUGS, and models themselves are created using the tsbugs package.


References in zbMATH (referenced in 171 articles )

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  1. Marsman, M.; Huth, K.; Waldorp, L. J.; Ntzoufras, I.: Objective Bayesian edge screening and structure selection for Ising networks (2022)
  2. Hadj-Amar, Beniamino; Finkenstädt, Bärbel; Fiecas, Mark; Huckstepp, Robert: Identifying the recurrence of sleep apnea using a harmonic hidden Markov model (2021)
  3. Lu, Peter; Lermusiaux, Pierre F. J.: Bayesian learning of stochastic dynamical models (2021)
  4. Bürkner, Paul-Christian; Gabry, Jonah; Vehtari, Aki: Approximate leave-future-out cross-validation for Bayesian time series models (2020)
  5. Dinner, Aaron R.; Thiede, Erik H.; Koten, Brian Van; Weare, Jonathan: Stratification as a general variance reduction method for Markov chain Monte Carlo (2020)
  6. Mulder, Kees; Klugkist, Irene; van Renswoude, Daan; Visser, Ingmar: Mixtures of peaked power Batschelet distributions for circular data with application to saccade directions (2020)
  7. Reichl, Johannes: Estimating marginal likelihoods from the posterior draws through a geometric identity (2020)
  8. Wong, Jackie S. T.; Forster, Jonathan J.; Smith, Peter W. F.: Properties of the bridge sampler with a focus on splitting the MCMC sample (2020)
  9. Annis, Jeffrey; Evans, Nathan J.; Miller, Brent J.; Palmeri, Thomas J.: Thermodynamic integration and steppingstone sampling methods for estimating Bayes factors: a tutorial (2019)
  10. Chakraborty, Saptarshi; Khare, Kshitij: Consistent estimation of the spectrum of trace class data augmentation algorithms (2019)
  11. Frühwirth-Schnatter, Sylvia: Keeping the balance -- bridge sampling for marginal likelihood estimation in finite mixture, mixture of experts and Markov mixture models (2019)
  12. Gronau, Quentin F.; Wagenmakers, Eric-Jan; Heck, Daniel W.; Matzke, Dora: A simple method for comparing complex models: Bayesian model comparison for hierarchical multinomial processing tree models using Warp-III bridge sampling (2019)
  13. Liu, Yang; Hu, Guanyu; Cao, Lei; Wang, Xiaojing; Chen, Ming-Hui: Rejoinder: A comparison of Monte Carlo methods for computing marginal likelihoods of item response theory models (2019)
  14. Liu, Yang; Yang, Ji Seung; Maydeu-Olivares, Alberto: Restricted recalibration of item response theory models (2019)
  15. Roussel, Julien; Stoltz, Gabriel: A perturbative approach to control variates in molecular dynamics (2019)
  16. Veen, Duco; Klugkist, Irene: Standard errors, priors, and bridge sampling: a discussion of Liu et al. (2019)
  17. Wang, Yu-Bo; Chen, Ming-Hui; Kuo, Lynn; Lewis, Paul O.: Partition weighted approach for estimating the marginal posterior density with applications (2019)
  18. Zens, Gregor: Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership (2019)
  19. Alzahrani, Naif; Neal, Peter; Spencer, Simon E. F.; McKinley, Trevelyan J.; Touloupou, Panayiota: Model selection for time series of count data (2018)
  20. Bouranis, Lampros; Friel, Nial; Maire, Florian: Bayesian model selection for exponential random graph models via adjusted pseudolikelihoods (2018)

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