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. 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)
  2. Vitoratou, Silia; Ntzoufras, Ioannis; Moustaki, Irini: Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions (2016)
  3. Waggoner, Daniel F.; Wu, Hongwei; Zha, Tao: Striated Metropolis-Hastings sampler for high-dimensional models (2016)
  4. White, Arthur; Wyse, Jason; Murphy, Thomas Brendan: Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler (2016)
  5. Bauwens, Luc; Dufays, Arnaud; Rombouts, Jeroen V.K.: Marginal likelihood for Markov-switching and change-point GARCH models (2014)
  6. Castellano, Rosella; Scaccia, Luisa: Can CDS indexes signal future turmoils in the stock market? A Markov switching perspective (2014)
  7. Friel, Nial; Hurn, Merrilee; Wyse, Jason: Improving power posterior estimation of statistical evidence (2014)
  8. Wang, Jing; Atchadé, Yves F.: Approximate Bayesian computation for exponential random graph models for large social networks (2014)
  9. Chatterjee, Sourav; Diaconis, Persi: Estimating and understanding exponential random graph models (2013)
  10. Fuentes-Albero, Cristina; Melosi, Leonardo: Methods for computing marginal data densities from the Gibbs output (2013)
  11. Ghosh, Joyee; Reiter, Jerome P.: Secure Bayesian model averaging for horizontally partitioned data (2013)
  12. 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)
  13. Ardia, David; Baştürk, Nalan; Hoogerheide, Lennart; Van Dijk, Herman K.: A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood (2012)
  14. Bauwens, Luc; Rombouts, Jeroen V.K.: On marginal likelihood computation in change-point models (2012)
  15. Deschamps, Philippe J.: Bayesian estimation of generalized hyperbolic skewed student GARCH models (2012)
  16. Doss, Hani: Hyperparameter and model selection for nonparametric Bayes problems via Radon-Nikodym derivatives (2012)
  17. Fiorentini, Gabriele; Planas, Christophe; Rossi, Alessandro: The marginal likelihood of dynamic mixture models (2012)
  18. Li, Zhen; Gopal, Vikneswaran; Li, Xiaobo; Davis, John M.; Casella, George: Simultaneous SNP identification in association studies with missing data (2012)
  19. Rouder, Jeffrey N.; Morey, Richard D.; Speckman, Paul L.; Province, Jordan M.: Default Bayes factors for ANOVA designs (2012)
  20. Song, Xin-Yuan; Lee, Sik-Yum: A tutorial on the Bayesian approach for analyzing structural equation models (2012)

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