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 146 articles )

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  1. Alzahrani, Naif; Neal, Peter; Spencer, Simon E. F.; McKinley, Trevelyan J.; Touloupou, Panayiota: Model selection for time series of count data (2018)
  2. 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)
  3. Wong, Jackie S. T.; Forster, Jonathan J.; Smith, Peter W. F.: Bayesian mortality forecasting with overdispersion (2018)
  4. Everitt, Richard G.; Johansen, Adam M.; Rowing, Ellen; Evdemon-Hogan, Melina: Bayesian model comparison with un-normalised likelihoods (2017)
  5. 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)
  6. Vitoratou, Silia; Ntzoufras, Ioannis: Thermodynamic Bayesian model comparison (2017)
  7. 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)
  8. Vitoratou, Silia; Ntzoufras, Ioannis; Moustaki, Irini: Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions (2016)
  9. Waggoner, Daniel F.; Wu, Hongwei; Zha, Tao: Striated Metropolis-Hastings sampler for high-dimensional models (2016)
  10. White, Arthur; Wyse, Jason; Murphy, Thomas Brendan: Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler (2016)
  11. Fang, Q.; Piegorsch, W. W.; Simmons, Susan J.; Li, X.; Chen, C.; Wang, Y.: Bayesian model-averaged benchmark dose analysis via reparameterized quantal-response models (2015)
  12. Kim, Dong-hyuk: Flexible Bayesian analysis of first price auctions using a simulated likelihood (2015)
  13. Koskela, Jere; Jenkins, Paul; Spanò, Dario: Computational inference beyond Kingman’s coalescent (2015)
  14. Bauwens, Luc; Dufays, Arnaud; Rombouts, Jeroen V. K.: Marginal likelihood for Markov-switching and change-point GARCH models (2014)
  15. Cameron, Ewan; Pettitt, Anthony: Recursive pathways to marginal likelihood estimation with prior-sensitivity analysis (2014)
  16. Castellano, Rosella; Scaccia, Luisa: Can CDS indexes signal future turmoils in the stock market? A Markov switching perspective (2014)
  17. Friel, Nial; Hurn, Merrilee; Wyse, Jason: Improving power posterior estimation of statistical evidence (2014)
  18. Heaps, Sarah E.; Boys, Richard J.; Farrow, Malcolm: Computation of marginal likelihoods with data-dependent support for latent variables (2014)
  19. Lloyd, Louise K.; Forster, Jonathan J.: Modelling trends in road accident frequency -- Bayesian inference for rates with uncertain exposure (2014)
  20. Perrakis, Konstantinos; Ntzoufras, Ioannis; Tsionas, Efthymios G.: On the use of marginal posteriors in marginal likelihood estimation via importance sampling (2014)

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