Syllabus

1. Statistical Estimation and Testing
Statistical inference from a Bayesian and Classical perspective; review of basic concepts from probability theory; topics in distribution theory; hypothesis testing and issues in multiple comparisons; resampling methods; missing data or unobserved variables via EM algorithm. Lectures based on associated chapters of Sorenesen & Gianola and on supplementary materials. Applications and examples drawn from statistical genetics, genetic epidemiology and functional genomics.

2. Markov Chains and Stochastic Processes
Introduction to discrete Markov chain theory; Poisson processes; inference for Markov chains; Markov chain-based evolutionary models; models and theory of biological sequence comparison; BLAST; hidden Markov models; statistical issues in searching biological databases. Markov chain Monte Carlo algorithms. Lectures based on associated chapters in Sorensen & Gianola and on supplementary material. Applications and examples drawn from bioinformatics and evolutionary genetics.

3. Data Analysis and Visualization
Descriptive and predictive modeling; linear regression and multiple linear regression; logistic regression and classification; principal components analysis (PCA); cluster analysis; data visualization and model checking. Lectures based on selected chapters in Venables & Ripley and on supplementary material. Applications and examples drawn from functional genomics and genetic epidemiology.

Schedule

Fall 2003 Academic Calendar

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