Bayesian statistics in genetics: a guide for the uninitiated.

Statistical analyses are used in many fields of genetic research. Most geneticists are taught classical statistics, which includes hypothesis testing, estimation and the construction of confidence intervals; this framework has proved more than satisfactory in many ways. What does a Bayesian framework have to offer geneticists? Its utility lies in offering a more direct approach to some questions and the incorporation of prior information. It can also provide a more straightforward interpretation of results. The utility of a Bayesian perspective, especially for complex problems, is becoming increasingly clear to the statistics community; geneticists are also finding this framework useful and are increasingly utilizing the power of this approach.

Propagating the effects of missing data sources in a surveillance system.

Sporadically missing data sources in syndromic surveillance systems result in inaccurate counts and detection algorithm results. We examined how data quality issues relating to missing data sources propagate through a surveillance system and devised a method to track and visualize the resulting data quality issues.

Estimating the rate of evolution of the rate of molecular evolution.

A simple model for the evolution of the rate of molecular evolution is presented. With a Bayesian approach, this model can serve as the basis for estimating dates of important evolutionary events even in the absence of the assumption of constant rates among evolutionary lineages. The method can be used in conjunction with any of the widely used models for nucleotide substitution or amino acid replacement. It is illustrated by analyzing a data set of rbcL protein sequences.