Computer intensive statistical methods

R是S编程语言的开源实现。作为“R核心团队”的一员，我也是其中的一员。我特别感兴趣的是用户界面和图形。特别是，在一项努力中，我正在努力改善R的内部结构和用户创建的源代码之间的联系：这将提高R的可用性，并将使程序员性能的研究成为可能。另一个正在进行的努力是通过rgl包将3D图形纳入R。 在之前的工作中，我和多伦多大学的同事已经展示了如何以计算效率高的方式评估数百万个基因测试的重要性。 目前，我正在与一个学生研究两个相关的概念：条件显著性的近似和多个测试组合的近似。 另一个学生和我正在考虑选择最经济但信息量最大的模型。 我工作的第三个重点是完美的模拟。马尔可夫链蒙特卡罗（英语：Markov chain Monte Carlo）是一种用于从任意分布（例如贝叶斯推断中的后验分布）进行采样的通用计算技术。直到1995年底，人们认为在几乎所有情况下，这将产生分布仅近似于目标分布的样本。Propp和Wilson（1996）表明，在某些情况下，可以获得精确的样本;我的工作和几年前与绿色的工作将其扩展到分布上。 连续和无界的状态空间。此外，Propp和Wilson的思想可以扩展到其他方面，如随机微分方程的随机扩散的完美模拟。我和另一位学生正在为“随机扩散”（金融建模中常用的模型）开发最大值、最小值和跨越障碍时间的完美模拟。

R is an open source implementation of the S programming language. As a member of the "R Core Team", I share authorship in it. My particular interests are in user interface issues and graphics. In particular, in one effort I am working on improving the connections between the internals of R and the source code created by the user: this will improve the usability of R and will enable studies of programmer performance. Another ongoing effort is the incorporation of 3D graphics into R, through the rgl package. In previous work, colleagues at the University of Toronto and I have shown how to evaluate the significance of millions of genetic tests in a computationally efficient way. Currently with one student I am investigating two related ideas: approximation of conditional significance, and approximation of multiple testing combinations. Another student and I are looking into choosing the most economical but informative model. A third focus of my work will be perfect simulation. Markov chain Monte Carlo is a general computational technique for sampling from an arbitrary distribution (e.g. a posterior distribution in Bayesian inference). Until late 1995, it was thought that in almost all cases this would yield samples with distributions only approximating the target distribution. Propp and Wilson (1996) showed that in some cases it is possible to get exact samples; my work and work with Green several years ago extended this to distributions on continuous and unbounded state spaces. Moreover, Propp and Wilson's ideas can be extended to other contexts such as perfect simulation of stochastic diffusions that follow stochastic differential equations. Another student and I are developing perfect simulations of maxima, minima, and barrier crossing times for "stochastic diffusions", models which are commonly used in financial modelling.