Course: STAT 35450=HGEN 48600
Title: Fundamentals of Computational Biology: Models and Inference
Class Schedule: Sec 1: TR 4:20 PM–5:40 PM (Remote)
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