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Course: STAT 35450=HGEN 48600
Title: Statistical Inference and Stochastic Models for Computational Biologists
Instructor(s): John Novembre; Matthew Stephens
Class Schedule: Sec 01: TR 3:00–4:20 PM in Cummings 322
Office Hours:  
Textbook(s): Ross, Introduction to Probability Models (11th ed)
Description: This course 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 244 or equivalent and comfort with programming, or consent of instructor