ABSTRACT
Bayesian statistics experienced a Renaissance with the advent of Markov chain Monte Carlo simulation, which made possible the analysis of posterior distributions that are analytically intractable (Geman and Geman, 1984; Gelfand and Smith, 1990). Phylogenetics transitioned to a likelihood-based framework with Felsenstein’s seminal paper (Felsenstein, 1981), and Bayesian approaches to phylogenetics were introduced in the mid-1990s with papers by Rannala and Yang (1996) and Mau and Newton (1997). Bayesian phylogenetics then quickly took off with the introduction of several software packages, the most popular to date being MRBAYES (Huelsenbeck and Ronquist, 2001; Ronquist et al., 2012b) and BEAST (Drummond and Rambaut, 2007; Drummond et al., 2012), which made it possible for empirical practitioners to carry out analyses on their own data. These programs pointed out the power of the Bayesian approach, and subsequent publications illustrated how the Bayesian paradigm allowed practical evaluation of innovative models exhibiting unprecedented biological detail. Examples include relaxed-clock divergence time estimation (Thorne et al., 1998; Kishino et al., 2001; Thorne and Kishino, 2002a; Drummond et al., 2006; Drummond and Suchard, 2010), analyses of co-speciation (Huelsenbeck et al., 2000c), ancestral state estimation (Huelsenbeck and Bollback, 2001), population history from serial sampled sequences (Drummond et al., 2002; Heled and Drummond, 2008), character correlation (Huelsenbeck and Rannala, 2003; Pagel and Meade, 2006), stochastic character mapping (Huelsenbeck et al., 2003), evolutionary dependence among sites (Robinson et al., 2003), detection of positively selected sites (Huelsenbeck and Dyer, 2004; Huelsenbeck et al., 2006), allowing substitution model heterogeneity across sites (Lartillot and Philippe, 2004; Pagel and Meade, 2004), combined analyses of morphological
Algorithms, and
and sequence data (Nylander et al., 2004), joint estimation of phylogeny and alignment (Redelings and Suchard, 2005), and species tree estimation (Heled and Drummond, 2010), to name only a few.
