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- 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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