Brown University Center for Computational Molecular Biology

AM 2810: Discrete High-Dimensional Inferences in Genomics

  • AM 2810: Discrete High-Dimensional Inferences in Genomics
    Instructor: Charles Lawrence
    Time and Place: Course Schedule

    Genomics is revolutionizing biology and biomedicine and generated a mass of clearly relevant high-D data along with many important high-D discreet inference problems. This course will focus on statistical inference in molecular biology and genomics. Computational biology topics: Hidden Markov models, Change point models, sequence alignment, RNA secondary structure, tests of differential expression, and Statistical topics: Special characteristics of discrete high-D inference including Bayesian posterior inference; point estimation; interval estimation; hypothesis tests; model selection; and statistical decision theory. While some background in molecular biology is desirable, it is not required. Previous training in probability equivalent to that in AM 165 is required. For more information view the Course Catalog.
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