readme.bib

@article{Goodman2010,
  title = {Ensemble Samplers with Affine Invariance},
  author = {Goodman, Jonathan and Weare, Jonathan},
  date = {2010-01-31},
  journaltitle = {Communications in Applied Mathematics and Computational Science},
  volume = {5},
  pages = {65--80},
  publisher = {{Mathematical Sciences Publishers}},
  issn = {2157-5452},
  doi = {10.2140/camcos.2010.5.65},
  url = {https://msp.org/camcos/2010/5-1/p04.xhtml},
  urldate = {2021-01-22},
  number = {1}
}

@article{terBraak2008,
  title = {Differential {{Evolution Markov Chain}} with Snooker Updater and~Fewer Chains},
  author = {{{ter Braak}}, Cajo J. F. and Vrugt, Jasper A.},
  date = {2008-12-01},
  journaltitle = {Statistics and Computing},
  volume = {18},
  pages = {435--446},
  issn = {1573-1375},
  doi = {10.1007/s11222-008-9104-9},
  url = {https://doi.org/10.1007/s11222-008-9104-9},
  urldate = {2021-01-22},
  abstract = {Differential Evolution Markov Chain (DE-MC) is an adaptive MCMC algorithm, in which multiple chains are run in parallel. Standard DE-MC requires at least N=2d chains to be run in parallel, where d is the dimensionality of the posterior. This paper extends DE-MC with a snooker updater and shows by simulation and real examples that DE-MC can work for d up to 50–100 with fewer parallel chains (e.g.N=3) by exploiting information from their past by generating jumps from differences of pairs of past states. This approach extends the practical applicability of DE-MC and is shown to be about 5–26 times more efficient than the optimal Normal random walk Metropolis sampler for the 97.5\% point of a variable from a 25–50 dimensional Student t3 distribution. In a nonlinear mixed effects model example the approach outperformed a block-updater geared to the specific features of the model.},
  langid = {english},
  number = {4},
  options = {useprefix=true}
}