Discussion Lead: Bob Carpenter (Flatiron CCM)
Topics: (1) Goodman and Weare. 2010. Ensemble Samplers with Affine Invariance.
(2) Foreman-Mackey et al. 2013. emcee: The MCMC Hammer.
Links: (1) https://msp.org/camcos/2010/5-1/p04.xhtml
(2) https://iopscience.iop.org/article/10.1086/670067/pdf
Abstracts: (1) We propose a family of Markov chain Monte Carlo methods whose performance is unaffected by affine tranformations of space. These algorithms are easy to construct and require little or no additional computational overhead. They should be particularly useful for sampling badly scaled distributions. Computational tests show that the affine invariant methods can be significantly faster than standard MCMC methods on highly skewed distributions.
(2) We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010).