|Course:||STAT 35450=HGEN 48600|
|Title:||Fundamentals of Computational Biology: Models and Inference|
|Instructor(s):||John Novembre; Matthew Stephens|
|Class Schedule:||Sec 01: TR 3:30 PM–4:50 PM in Cummings 525|
|Textbook(s):||Ross, Introduction to Probability Models (11th ed)|
|Description:||Covers key principles in probability and statistics that are used to model and understand biological data. There will be a strong emphasis on stochastic processes and inference in complex hierarchical statistical models. Topics will vary but the typical content would include: Likelihood-based and Bayesian inference, Poisson processes, Markov models, Hidden Markov models, Gaussian Processes, Brownian motion, Birth-death processes, the Coalescent, Graphical models, Markov processes on trees and graphs, and Markov Chain Monte Carlo.
Prerequisite(s): STAT 24400