Matthew James Cockerill and Melissa Norton; Emma Veitch; An-Wen Chan, Ida Sim, A. Metin Gülmezoglu, Patrick Unterlerchner, Ghassan Karam, Tikki Pang; Response Celia B. Fisher
International Stem Cell Forum Ethics Working Party
Technical Comment Abstracts
Comment on “Phylogenetic MCMC Algorithms Are Misleading on Mixtures of Trees”
Fredrik Ronquist, Bret Larget, John P. Huelsenbeck, Joseph B. Kadane, Donald Simon, Paul van der Mark
Abstract: Mossel and Vigoda (Reports, 30 September 2005, p. 2207) show that nearest neighbor interchange transitions, commonly used in phylogenetic Markov chain Monte Carlo (MCMC) algorithms, perform poorly on mixtures of dissimilar trees. However, the conditions leading to their results are artificial. Standard MCMC convergence diagnostics would detect the problem in real data, and correction of the model misspecification would solve it.
Full text at www.sciencemag.org/cgi/content/full/312/5772/367a
Response to Comment on “Phylogenetic MCMC Algorithms Are Misleading on Mixtures of Trees”
Elchanan Mossel and Eric Vigoda
Abstract: We presented a tree mixture in which Markov chain Monte Carlo (MCMC) methods have an exponentially slow convergence rate. We expect that many other mixture scenarios will show slow convergence. Ronquist et al. show that Metropolis-coupled MCMC (MC3) converges quickly on our mixture. However, they presented no theoretical or systematic experimental evidence determining the type of mixtures where MC3 or other methods are efficient.
Full text at www.sciencemag.org/cgi/content/full/312/5772/367b