Rtwalk: The R Implementation of the ’t-walk’ MCMC Algorithm. The ’t-walk’ is a general-purpose MCMC sampler for arbitrary continuous distributions that requires no tuning.
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References in zbMATH (referenced in 10 articles )
Showing results 1 to 10 of 10.
- Rubio, F.J.: Letter to the editor: On the use of improper priors for the shape parameters of asymmetric exponential power models (2015)
- Capistrán, Marcos A.; Christen, J.Andrés; Velasco-Hernández, Jorge X.: Towards uncertainty quantification and inference in the stochastic SIR epidemic model (2012)
- Lucka, Felix: Fast Markov chain Monte Carlo sampling for sparse Bayesian inference in high-dimensional inverse problems using L1-type priors (2012)
- Martin, James; Wilcox, Lucas C.; Burstedde, Carsten; Ghattas, Omar: A stochastic Newton MCMC method for large-scale statistical inverse problems with application to seismic inversion (2012)
- Blaauw, Maarten; Christen, J.Andrés: Flexible paleoclimate age-depth models using an autoregressive gamma process (2011)
- Christen, J. Andrés; Sansó, Bruno: Advances in the sequential design of computer experiments based on active learning (2011)
- Rubio, F.J.; Steel, M.F.J.: Inference for grouped data with a truncated skew-Laplace distribution (2011)
- Christen, J.Andrés; Fox, Colin: A general purpose sampling algorithm for continuous distributions (the t-walk) (2010)
- Lieberman, Chad; Willcox, Karen; Ghattas, Omar: Parameter and state model reduction for large-scale statistical inverse problems (2010)
- Wikle, Christopher K.; Hooten, Mevin B.: A general science-based framework for dynamical spatio-temporal models (2010)