Introduction to Computer-Intensive Methods of Data Analysis in Biology

摘要

In many disciplines, scientists are interested in estimating characteristics of the underlying population whose sampling distributions are unknown. In these situations, inferences have traditionally made use of elaborate assumptions in order to make analytical approaches tractable. In these nonstandard situations, more desirable analytical approaches involve such computationally intensive methods as bootstrapping, Monte Carlo methods, and Bayesian tools. Despite the many advances in statistical software, prevalence of these computationally intensive methods is still limited. The presentation of these computationally intensive methods is sparse at best outside of statistics graduate courses. Unfortunately, these methods are often absent in the classrooms where they may be most needed: the graduate service courses to nonstatistics majors.