Designed for a one or two semester senior undergraduate or graduate bioinformatics course, Statistical Bioinformatics takes a broad view of the subject - not just gene expression and sequence analysis, but a careful balance of statistical theory in the context of bioinformatics applications. The inclusion of R code as well as the development of advanced methodology such as Bayesian and Markov models provides students with the important foundation needed to conduct bioinformatics.
目 錄
Ch 1: Introduction
Ch 2: Genomics
Ch 3: Probability and Statistical Theory
Ch 4: Special Distributions, Properties and Applications
Ch 5: Statistical Inference and Applications
Ch 6: Nonparametric Statistics
Ch 7: Bayesian Statistics
Ch 8: Markov Chain, Monte Carlo
Ch 9: Analysis of Variance
Ch 10: Design of Experiments
Ch 11: Multiple Testing of Hypotheses
