Probability and Statistics
This course introduces probability and statistics in the bioinformatics context, building a foundation for the "probabilistic thinking" method with applications to real life problems within biophysics, bioinformatics and data analysis. The course addresses probability theory with one and many random variables, classical and Bayesian methods, Poisson processes and Markov chains and applications to sequence analysis, gene finding and phylogenetics, and the fundamentals of the Mathematica programming language and its uses in computational probabilistic experiments.
At the end of the course, students will be able to:
Apply general principles of probability and statistics, including set theory, probability distributions, non-linear regressions, sequence analysis algorithms, and hypothesis testing, in the context of experimental data analysis, sequence analysis, and other bioinformatics problems.
Apply probabilistic methods and concepts, including Boltmann statistics, Monte Carlo and molecular dynamics modeling, Mendelian genetics, and models of statistical evolution, to the analysis of biological systems on different levels.
Participate in a team research work involving numerical statistical analysis and modeling, and communicate its results to colleagues; make presentations on various statistical topics.
Use Mathematica as a programming, visualization and presentation environment.