Statistics and Algorithms in Evolution

Tewari博士开发了具有Java可扩展的面向对象框架的计算资源，用于处理精确的合并递归和使用重要性采样进行Monte Carlo模拟。 我们已经提出了我们的组合的聚结结果的理解，从而在具体的改进聚结算法。特别是，对于聚结概率的重要性抽样，Tewari和Spouge博士在框架内应用了精确的概率计算，以将有效样本量增加约4倍，而计算时间的成本可以忽略不计。 博士Spouge还开发了一些与条形码采样策略相关的合并结果，在他的DNA条形码项目（LM 200883）中报告。 作为这项研究的副产品，与Drs da Silva和Harris的合作开发了一种分析疟疾寄生虫基因组的新统计方法，从而得出了一些关于疟疾物种形成的新结论。

Dr Tewari has developed computational resources with an expansible object-oriented framework in Java for handling exact coalescent recursions and doing Monte Carlo simulations with importance sampling. We have advanced our combinatorial understanding of coalescent results, resulting in concrete improvements to coalescent algorithms. In particular, for importance sampling of coalescent probabilities, Drs. Tewari and Spouge have applied exact probability calculations within the framework to increase the effective sample size by about four with negligible costs in computation time. Dr. Spouge has also developed some coalescent results relevant to barcode sampling strategies, reported in his project on DNA barcodes (LM200883). As a byproduct of this research, a collaboration with Drs da Silva and Harris developed a new statistical method of analyzing genomes of malaria parasites, resulting in some novel conclusions about the speciation of malaria.