The goal of this course is to familiarize the students with statistical models and approaches that find applications in computational biology. We will cover some notions of Probability (in order to calculate the significance levels at the base of BLAST) , Bayesian inference, Markov Chain Monte Carlo sampling, Expectation Maximization (EM) algorithm, linear regression, model selection, information theory and minimum description length criteria, the bootstrap, False Discovery Rate (FDR) for control of errors in case of multiple comparison. Each topic will be introduced with specific reference to one problem of computational biology. Articles from the litterature and introductory material will be collected in a course reader.
In the fall 2005, there will be two make up classes:
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