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.

References in zbMATH (referenced in 22 articles , 1 standard article )

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  1. Christen, J. Andrés; Pérez-Garmendia, José Luis: Weak and TV consistency in Bayesian uncertainty quantification using disintegration (2021)
  2. Acuña-Zegarra, Manuel Adrian; Santana-Cibrian, Mario; Velasco-Hernandez, Jorge X.: Modeling behavioral change and COVID-19 containment in Mexico: a trade-off between lockdown and compliance (2020)
  3. Alawieh, Leen; Goodman, Jonathan; Bell, John B.: Iterative construction of Gaussian process surrogate models for Bayesian inference (2020)
  4. Christen, J. Andrés; Parker, Albert E.: Systematic statistical analysis of microbial data from dilution series (2020)
  5. Dolgov, Sergey; Anaya-Izquierdo, Karim; Fox, Colin; Scheichl, Robert: Approximation and sampling of multivariate probability distributions in the tensor train decomposition (2020)
  6. Dufays, Arnaud; Rombouts, Jeroen V. K.: Relevant parameter changes in structural break models (2020)
  7. Morzfeld, M.; Tong, X. T.; Marzouk, Y. M.: Localization for MCMC: sampling high-dimensional posterior distributions with local structure (2019)
  8. Aquino-López, Marco A.; Blaauw, Maarten; Christen, J. Andrés; Sanderson, Nicole K.: Bayesian analysis of (^210\mathrmPb) dating (2018)
  9. Leimkuhler, Benedict; Matthews, Charles; Weare, Jonathan: Ensemble preconditioning for Markov chain Monte Carlo simulation (2018)
  10. Villa, Cristiano; Rubio, Francisco J.: Objective priors for the number of degrees of freedom of a multivariate (t) distribution and the (t)-copula (2018)
  11. Rubio, Francisco J.; Yu, Keming: Flexible objective Bayesian linear regression with applications in survival analysis (2017)
  12. Capistrán, Marcos A.; Christen, J. Andrés; Donnet, Sophie: Bayesian analysis of ODEs: solver optimal accuracy and Bayes factors (2016)
  13. Rodríguez-Narciso, Silvia; Christen, J. Andrés: Optimal sequential Bayesian analysis for degradation tests (2016)
  14. Rubio, F. J.: Letter to the editor: On the use of improper priors for the shape parameters of asymmetric exponential power models (2015)
  15. Rubio, F. J.; Steel, M. F. J.: Bayesian modelling of skewness and kurtosis with two-piece scale and shape distributions (2015)
  16. Capistrán, Marcos A.; Christen, J. Andrés; Velasco-Hernández, Jorge X.: Towards uncertainty quantification and inference in the stochastic SIR epidemic model (2012)
  17. Lucka, Felix: Fast Markov chain Monte Carlo sampling for sparse Bayesian inference in high-dimensional inverse problems using L1-type priors (2012)
  18. Blaauw, Maarten; Christen, J. Andrés: Flexible paleoclimate age-depth models using an autoregressive gamma process (2011)
  19. Christen, J. Andrés; Sansó, Bruno: Advances in the sequential design of computer experiments based on active learning (2011)
  20. Rubio, F. J.; Steel, M. F. J.: Inference for grouped data with a truncated skew-Laplace distribution (2011)

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