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.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 160 articles )

Showing results 1 to 20 of 160.
Sorted by year (citations)

1 2 3 ... 6 7 8 next

  1. Mulder, Kees; Klugkist, Irene; van Renswoude, Daan; Visser, Ingmar: Mixtures of peaked power Batschelet distributions for circular data with application to saccade directions (2020)
  2. Annis, Jeffrey; Evans, Nathan J.; Miller, Brent J.; Palmeri, Thomas J.: Thermodynamic integration and steppingstone sampling methods for estimating Bayes factors: a tutorial (2019)
  3. Chakraborty, Saptarshi; Khare, Kshitij: Consistent estimation of the spectrum of trace class data augmentation algorithms (2019)
  4. Frühwirth-Schnatter, Sylvia: Keeping the balance -- bridge sampling for marginal likelihood estimation in finite mixture, mixture of experts and Markov mixture models (2019)
  5. 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)
  6. 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)
  7. Liu, Yang; Yang, Ji Seung; Maydeu-Olivares, Alberto: Restricted recalibration of item response theory models (2019)
  8. Roussel, Julien; Stoltz, Gabriel: A perturbative approach to control variates in molecular dynamics (2019)
  9. Veen, Duco; Klugkist, Irene: Standard errors, priors, and bridge sampling: a discussion of Liu et al. (2019)
  10. Zens, Gregor: Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership (2019)
  11. Alzahrani, Naif; Neal, Peter; Spencer, Simon E. F.; McKinley, Trevelyan J.; Touloupou, Panayiota: Model selection for time series of count data (2018)
  12. Koskinen, Johan; Wang, Peng; Robins, Garry; Pattison, Philippa: Outliers and influential observations in exponential random graph models (2018)
  13. Touloupou, Panayiota; Alzahrani, Naif; Neal, Peter; Spencer, Simon E. F.; McKinley, Trevelyan J.: Efficient model comparison techniques for models requiring large scale data augmentation (2018)
  14. Wong, Jackie S. T.; Forster, Jonathan J.; Smith, Peter W. F.: Bayesian mortality forecasting with overdispersion (2018)
  15. Everitt, Richard G.; Johansen, Adam M.; Rowing, Ellen; Evdemon-Hogan, Melina: Bayesian model comparison with un-normalised likelihoods (2017)
  16. Gronau, Quentin F.; Sarafoglou, Alexandra; Matzke, Dora; Ly, Alexander; Boehm, Udo; Marsman, Maarten; Leslie, David S.; Forster, Jonathan J.; Wagenmakers, Eric-Jan; Steingroever, Helen: A tutorial on bridge sampling (2017)
  17. Vitoratou, Silia; Ntzoufras, Ioannis: Thermodynamic Bayesian model comparison (2017)
  18. Hug, Sabine; Schwarzfischer, Michael; Hasenauer, Jan; Marr, Carsten; Theis, Fabian J.: An adaptive scheduling scheme for calculating Bayes factors with thermodynamic integration using Simpson’s rule (2016)
  19. Vitoratou, Silia; Ntzoufras, Ioannis; Moustaki, Irini: Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions (2016)
  20. Waggoner, Daniel F.; Wu, Hongwei; Zha, Tao: Striated Metropolis-Hastings sampler for high-dimensional models (2016)

1 2 3 ... 6 7 8 next