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# Updating formula and a pairwise algorithm for computing sample variances. Algorithms for calculating variance.

Biologists who have used phylogenetic software programs to analyze their own data will find the book particularly rewarding, although it should appeal to anyone seeking an authoritative overview of this exciting area of computational biology. That's what I get for not scrolling down I havn't checked the math whether it allows negative weights though, but at a first look it should! The appendixes are a good source of information to understand how numerical analysis works in phylogenetics. I'm slightly familiar with the Chan et al approach, but thought of it as a one-pass method for computing a single variance over an entire sample, with the added advantage that the problem can be broken into parts that are run in parallel. However, on skewed data sets, the behaviour might be different. I have added this experiment as unit test to ELKI, you can see the full source here: Computational Molecular Evolution provides an up-to-date and comprehensive coverage of modern statistical and computational methods used in molecular evolutionary analysis, such as maximum likelihood and Bayesian statistics. Yang describes the models, methods and algorithms that are most useful for analysing the ever-increasing supply of molecular sequence data, with a view to furthering our understanding of the evolution of genes and genomes. The increasing availability of large genomic data sets requires powerful statistical methods to analyse and interpret them, generating both computational and conceptual challenges for the field. The book emphasizes essential concepts rather than mathematical proofs.

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