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## Bayesian inference in phylogeny
## Additional recommended knowledge
## Basic Bayesian TheoryRecall that for Bayesian inference:
The denominator is the
where is the parameter space for . In the original Metropolis algorithm, given a current -value , and a new -value , the new value is accepted with probability: ## The LOCAL algorithm of Larget and SimonThe LOCAL algorithm begins by selecting an internal branch of the tree at random. The nodes at the ends of this branch are each connected to two other branches. One of each pair is chosen at random. Imagine taking these three selected edges and stringing them like a clothesline from left to right, where the direction (left/right) is also selected at random. The two endpoints of the first branch selected will have a sub-tree hanging like a piece of clothing strung to the line. The algorithm proceeds by multiplying the three selected branches by a common random amount, akin to stretching or shrinking the clothesline. Finally the leftmost of the two hanging sub-trees is disconnected and reattached to the clothesline at a location selected uniformly at random. Suppose we began by selecting the internal branch with length $$ (in Figure (a) (to be added)) that separates taxa and from the rest. Suppose also that we have (randomly) selected branches with lengths and from each side, and that we oriented these branches as shown in Figure(b). Let , be the current length of the clothesline. We select the new length to be , where is a uniform random variable on . Then for the LOCAL algorithm, the acceptance probability can be computed to be: ## Assessing ConvergenceSuppose we want to estimate a branch length of a 2-taxon tree under JC, in which for unvaried sites, and Thus the unnormalized posterior distribution is: or, alternately, Update branch length by choosing new value uniformly at random from a window of half-width centered at the current value: where is uniformly distributed between and . The acceptance probability is: Example: , . We will compare results for two values of , and . In each case, we will begin with an initial length of and update the length times. (See Figure 3.2 (to be added) for results.) ## Metropolis-coupled MCMC (Geyer)If the target distribution has multiple peaks, separated by low valleys, the Markov chain may have difficulty in moving from one peak to another. As a result, the chain may get stuck on one peak and the resulting samples will not approximate the posterior density correctly. This is a serious practical concern for phylogeny reconstruction, as multiple local peaks are known to exist in the tree space during heuristic tree search under maximum parsimony (MP), maximum likelihood (ML), and minimum evolution (ME) criteria, and the same can be expected for stochastic tree search using MCMC. Many strategies have been proposed to improve mixing of Markov chains in presence of multiple local peaks in the posterior density. One of the most successful algorithms is the Metropolis-coupled MCMC (or ). In this algorithm, $m$ chains are run in parallel, with different stationary distributions , $$, where the first one, is the target density, while , are chosen to improve mixing. For example, one can choose incremental heating of the form: so that the first chain is the cold chain with the correct target density, while chains are heated chains. Note that raising the density π(.) to the power with has the effect of flattening out the distribution, similar to heating a metal. In such a distribution, it is easier to traverse between peaks (separated by valleys) than in the original distribution. After each iteration, a swap of states between two randomly chosen chains is proposed through a Metropolis-type step. Let be the current state in chain , . A swap between the states of chains and is accepted with probability: At the end of the run, output from only the cold chain is used, while those from the hot chains are discarded. Heuristically, the hot chains will visit the local peaks rather easily, and swapping states between chains will let the cold chain occasionally jump valleys, leading to better mixing. However, if is unstable, proposed swaps will seldom be accepted. This is the reason for using several chains which differ only incrementally. (See Figure3.3 (to be added)). An obvious disadvantage of the algorithm is that chains are run and only one chain is used for inference. For this reason, is ideally suited for implementation on parallel machines, since each chain will in general require the same amount of computation per iteration.
## References- Geyer, C.J. (1991) Markov chain Monte Carlo maximum likelihood. In
*Computing Science and Statistics: Proceedings of the 23rd Symposium of the Interface*(ed. E.M. Keramidas), pp. 156-163. Interface Foundation, Fairfax Station, VA. - Yang, Z. and B. Rannala. (1997) Bayesian phylogenetic inference using DNA sequences: A Markov chain Monte Carlo method.
*Molecular Biology and Evolution*,**14**, 717-724. - Larget, B. and D.L. Simon. (1999) Markov chain Monte Carlo algorithms for the Bayesian analysis of phylogenetic trees.
*Molecular Biology and Evolution*,**16**, 750-759. - Huelsenbeck, J.P. and F. Ronquist. (2001) MrBayes: Bayesian inference in phylogenetic trees.
*Bioinformatics*,**17**, 754-755. - Ronquist, F. and J.P. Huelsenbeck. (2003) MrBayes3: Bayesian phylogenetic inference under mixed models.
*Bioinformatics*,**19**, 1572-1574. - Rannala, B. and Z. Yang. (2003) Bayes estimation of species divergence times and ancestral population sizes using DNA sequences from multiple loci.
*Genetics*,**164**, 1645-1656.
Categories: Bioinformatics | Phylogenetics | Computational phylogenetics |
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This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Bayesian_inference_in_phylogeny". A list of authors is available in Wikipedia. |