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
References in zbMATH (referenced in 129 articles )
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- Everitt, Richard G.; Johansen, Adam M.; Rowing, Ellen; Evdemon-Hogan, Melina: Bayesian model comparison with un-normalised likelihoods (2017)
- Vitoratou, Silia; Ntzoufras, Ioannis: Thermodynamic Bayesian model comparison (2017)
- 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)
- Vitoratou, Silia; Ntzoufras, Ioannis; Moustaki, Irini: Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions (2016)
- Waggoner, Daniel F.; Wu, Hongwei; Zha, Tao: Striated Metropolis-Hastings sampler for high-dimensional models (2016)
- White, Arthur; Wyse, Jason; Murphy, Thomas Brendan: Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler (2016)
- Bauwens, Luc; Dufays, Arnaud; Rombouts, Jeroen V.K.: Marginal likelihood for Markov-switching and change-point GARCH models (2014)
- Castellano, Rosella; Scaccia, Luisa: Can CDS indexes signal future turmoils in the stock market? A Markov switching perspective (2014)
- Friel, Nial; Hurn, Merrilee; Wyse, Jason: Improving power posterior estimation of statistical evidence (2014)
- Wang, Jing; Atchadé, Yves F.: Approximate Bayesian computation for exponential random graph models for large social networks (2014)
- Chatterjee, Sourav; Diaconis, Persi: Estimating and understanding exponential random graph models (2013)
- Fuentes-Albero, Cristina; Melosi, Leonardo: Methods for computing marginal data densities from the Gibbs output (2013)
- Ghosh, Joyee; Reiter, Jerome P.: Secure Bayesian model averaging for horizontally partitioned data (2013)
- 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)
- Ardia, David; Baştürk, Nalan; Hoogerheide, Lennart; Van Dijk, Herman K.: A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood (2012)
- Bauwens, Luc; Rombouts, Jeroen V.K.: On marginal likelihood computation in change-point models (2012)
- Deschamps, Philippe J.: Bayesian estimation of generalized hyperbolic skewed student GARCH models (2012)
- Doss, Hani: Hyperparameter and model selection for nonparametric Bayes problems via Radon-Nikodym derivatives (2012)
- Fiorentini, Gabriele; Planas, Christophe; Rossi, Alessandro: The marginal likelihood of dynamic mixture models (2012)
- Li, Zhen; Gopal, Vikneswaran; Li, Xiaobo; Davis, John M.; Casella, George: Simultaneous SNP identification in association studies with missing data (2012)