Bayesian Statistical In erence in the Sciences

"Fundamental principles and techniques of probability, statistical inference and data analysis, as pertains to the sciences, especially bioinformatics. Random variables and their distributions. Central limit theorem. Conditional probability, Markov chains and Hidden Markov Models. Bayesian statistical paradigm and inference using Markov chain Monte Carlo. Computer simulations and data analysis. This new course will cover intermediate-level topics in probability theory and statistcal inference, with an emphasis on Bayesian statiscal inference. It is designed for students who have sufficient mathematical background, and who may want to devote just one semester learning about the use of probability and statistics in the sciences, in engineering, and especially in the emerging field of bioinformatics. It is intended for biology students interested in the quantitative and computational aspects of their discipline, and for computer science students seeking to explore the theoretical foundations of data mining and structural learning. Thus, such students would have available to them a one-semester course that covers healthy combination of the main ST

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