| 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 |
| Office Hours: | |
| 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 |