The course provides an introduction to likelihood-based inference in Biology. We will cover both theoretical and practical aspects of maximum likelihood and Bayesian inference.

Meeting: Monday and Wednesday 10:00-10:50AM in 2025 Haworth

The syllabus is available as a pdf by clicking here.

Instructors (office hours by appointment):

Ford Ballantyne 864-1868 154 Higuchi
John Kelly 864-3706 5005 Haworth
Mark Holder 864-5789 6031 Haworth

Grades will be based on class participation and homework assignments. We will have approximately one homework assignment per week.


Date Topic and Links Assignments
Week 1 Jan 24, Jan 26 Probability, random variables, distributions
Week 2 Jan 31, Feb 02 Random samples, sample distributions, likelihood
Week 3 Feb 07, Feb 09 Explicitly specifying variability: likelihood examples and Bayes' rule.
Notes on probability by Bálint Tóth
Assignment 1 (due Wed. Feb 16th)
Week 4 Feb 14, Feb 16 Likelihood examples and maximum likelihood estimation Assignment 2 (due Wed. Feb 23rd).
Week 5 Feb 21, Feb 23 Review / Likelihoo ratio test statistic
Week 6 Feb 28, Mar 02 Model Selection/Parametric bootstrapping

Some notes from lecture..
Assignment 3.
The example to contemplate for Monday's class
Week 7 Mar 07, Mar 09 Computational aspects: numerical optimization.

Notes on sufficiency and identifiability
Week 8 Mar 14, Mar 16 Computational aspects: numerical optimization (continued)
generalMismatchedFights.R.txt - R script - Python script - a more general Python script
simpleData.txt - data set used by the R and "generic" Python script

numerical_opt.pdf notes.

2011_LHM_HW4.txt the homework assignment. (probably the best python template for the homework).
Mar 21 - Mar 27 SPRING BREAK
Week 9 Mar 28, Mar 30 Generalized Linear Models Homework 5 2011_LHM_HW5.pdf
Homework 5 Excel spreadsheat CAD_data.xls
Template for the homework:
Week 10 Apr 04, Apr 06 Generalized Linear Models (continued)
Week 11 Apr 11, Apr 13 Generalized Linear Models (continued)
Week 12 Apr 18, Apr 20 more Generalized Linear Models Homework 6 (due Wed. Apr 27th): HW6.doc Updated Apr. 21
Data file: skinks_eat_bugs.csv
Template: Updated Apr. 23
Week 13 Apr 25, Apr 27 Computational aspects of Bayesian inference: Markov chain Monte Carlo (MCMC).
Some notes: 2011_lhm_bayesian_mcmc_1.pdf
Week 14 May 02, May 04 Multiparameter MCMC. latent_variable_MCMC.pdf (notes)
Code for MCMC over the five parameters:
Code for MCMC over the five parameters and the latent variables:
Slides for Wed, May 4th lecture latentVarSlides.pdf
Peter Green posts a link to "Trans-dimensional Markov chain Monte Carlo" on his site:
Homework 7: 2011_LHM_HW7.pdf
the data: fertilization_data.csv
the (hopefully) easier template to use:
Week 15 May 09, May 11 Hastings ratio and model jumping
Some slides: hastingsRatio.pdf
More slides: bayesFactorLecture.pdf
Homework 8: 2011_LHM_HW8.pdf