The t-walk is a ”A General Purpose Sampling Algorithm for Continuous Distributions” to sample from many objective functions (specially suited for posterior distributions using non-standard models that would make the use of common algorithms and software difficult); it is an MCMC that does not required tuning. However, as mentioned in the paper, it may not perform well in some examples and fine tuned samplers to specific objective densities should perform better than the t-walk. It is now implemented in Python, R, C++, C (native stand alone) and MatLab, see below.
Keywords for this software
References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
- Capistrán, Marcos A.; Christen, J.Andrés; Donnet, Sophie: Bayesian analysis of ODEs: solver optimal accuracy and Bayes factors (2016)
- 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)
- Blaauw, Maarten; Christen, J.Andrés: Flexible paleoclimate age-depth models using an autoregressive gamma process (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)
- Wikle, Christopher K.; Hooten, Mevin B.: A general science-based framework for dynamical spatio-temporal models (2010)