Computational methodology for the inference of genealogical structure from genetic data.

马尔可夫链蒙特卡罗方法吸引了大量的 对统计学的许多领域感兴趣。 它们提供 从概率分布的实现只知道， 一个正常化常数，因此从棘手的 潜变量条件概率分布 根据观察数据。 这些实现可用于 获得蒙特卡罗近似太难 似然函数和后验概率 分布。 这些方法允许勘探大型和 模型和参数的复杂假设空间。 从遗传数据中估计谱系结构是一种 长期存在的问题，最近引起了新的兴趣 随着新型数据的出现， 信息量更大这种材料的供应和数量 数据正在迅速增加。 家谱的估计 随着经济的发展， 越来越有必要评估详细的人口 在数量严重受限的人群中， 高度濒危物种。A中的主要问题 谱系结构谎言的相干统计分析 在众多的替代性家谱假设中， 可以将一组个体联系起来。的发展 使用马尔可夫链蒙特卡罗方法对 系谱推断与人口估计 结构为这个复杂的问题提供了解决方案。的 目前的奖项将支持研究，这两个 方法和计算。 Markov chain Monte CarloMethod非常灵活;开发一个有效的 解决一类特殊问题的方法不是常规的。 将制定和评估方法。 软件 实施的方法将被开发，并将被应用 在一些可用数据集的背景下。 这个奖项是 由四个项目支持：统计和 概率、计算数学、系统学和 人口生物学和计算生物学。

Methods of Markov chain Monte Carlo are attracting great interest in many areas of Statistics. They provide realizations from probability distributions known only up to a normalizing constant, and thus from intractable probability distributions of latent variables conditional upon observed data. These realizations can be used to obtain Monte Carlo approximants too therwise intractable likelihood functions and posterior probability distributions. The methods permit exploration of large and complex hypothesis spaces of models and parameters. Estimation of genealogical structure from genetic data is a long-standing problem, recently attracting renewed interest with the advent of new types of data that are potentially much more informative. The availability and quantity of such data are increasing rapidly. The estimation of genealogies becomes of increasing practical importance with the increasing necessity to evaluate detailed population structure in the severely numerically restricted populations of highly endangered species. The major problem in a coherent statistical analysis of genealogical structure lies in the plethora of alternative genealogical hypotheses that could relate a set of individuals. The development of the use of Markov chain Monte Carlo methods towards the inference of genealogies and the estimation of population structure offers a solution of this complex problem. The current award will support research that is both methodological and computational. Markov chain Monte CarloMethods are very flexible; developing an effective method for a particular class of problems is not routine. Methods will be developed and evaluated. Software to implement the methods will be developed, and will be applied in the context of some available data sets. This award is being supported by four programs: Statistics and Probability, Computational Mathematics, Systematics and Population Biology, and Computational Biology.