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.

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  1. Everitt, Richard G.; Johansen, Adam M.; Rowing, Ellen; Evdemon-Hogan, Melina: Bayesian model comparison with un-normalised likelihoods (2017)
  2. 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)
  3. Vitoratou, Silia; Ntzoufras, Ioannis: Thermodynamic Bayesian model comparison (2017)
  4. 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)
  5. Vitoratou, Silia; Ntzoufras, Ioannis; Moustaki, Irini: Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions (2016)
  6. Waggoner, Daniel F.; Wu, Hongwei; Zha, Tao: Striated Metropolis-Hastings sampler for high-dimensional models (2016)
  7. White, Arthur; Wyse, Jason; Murphy, Thomas Brendan: Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler (2016)
  8. Fang, Q.; Piegorsch, W.W.; Simmons, S.J.; Li, X.; Chen, C.; Wang, Y.: Bayesian model-averaged benchmark dose analysis via reparameterized quantal-response models (2015)
  9. Koskela, Jere; Jenkins, Paul; Spanò, Dario: Computational inference beyond Kingman’s coalescent (2015)
  10. Bauwens, Luc; Dufays, Arnaud; Rombouts, Jeroen V.K.: Marginal likelihood for Markov-switching and change-point GARCH models (2014)
  11. Cameron, Ewan; Pettitt, Anthony: Recursive pathways to marginal likelihood estimation with prior-sensitivity analysis (2014)
  12. Castellano, Rosella; Scaccia, Luisa: Can CDS indexes signal future turmoils in the stock market? A Markov switching perspective (2014)
  13. Friel, Nial; Hurn, Merrilee; Wyse, Jason: Improving power posterior estimation of statistical evidence (2014)
  14. Wang, Jing; Atchadé, Yves F.: Approximate Bayesian computation for exponential random graph models for large social networks (2014)
  15. Chatterjee, Sourav; Diaconis, Persi: Estimating and understanding exponential random graph models (2013)
  16. Dutta, Ritabrata; Ghosh, Jayanta K.: Bayes model selection with path sampling: factor models and other examples (2013)
  17. Fuentes-Albero, Cristina; Melosi, Leonardo: Methods for computing marginal data densities from the Gibbs output (2013)
  18. Ghosh, Joyee; Reiter, Jerome P.: Secure Bayesian model averaging for horizontally partitioned data (2013)
  19. Yu, Philip L.H.; Lee, Paul H.; Wan, W.M.: Factor analysis for paired ranked data with application on parent-child value orientation preference data (2013)
  20. Ardia, David; Baştürk, Nalan; Hoogerheide, Lennart; Van Dijk, Herman K.: A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood (2012)

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