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- AM 2810: Discrete High-Dimensional
Inferences in Genomics
Instructor: Charles Lawrence
Time: TBD
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.
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