The course provides an introduction to likelihoodbased inference in Biology. We will cover both theoretical and practical aspects of maximum likelihood and Bayesian inference.
Meeting: Monday and Wednesday 10:0010:50AM in 2025 Haworth
The syllabus is available as a pdf by clicking here.
Instructors (office hours by appointment):
Ford Ballantyne  fb4@ku.edu  8641868  154 Higuchi 
John Kelly  jkk@ku.edu  8643706  5005 Haworth 
Mark Holder  mtholder@ku.edu  8645789  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 generalMismatchedFights.py.txt  Python script genericMultiParamLRTest.py.txt  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. templateParametricBoot.py.txt (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: hw5templateParametricBoot.py.txt 
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: template_HW6.py.txt 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: gekko_glm_mcmc.py.txt Code for MCMC over the five parameters and the latent variables: latent_gekko_svl.py.txt Slides for Wed, May 4th lecture latentVarSlides.pdf Peter Green posts a link to "Transdimensional Markov chain Monte Carlo" on his site: http://www.maths.bris.ac.uk/~mapjg/Papers.html 
Homework 7: 2011_LHM_HW7.pdf the data: fertilization_data.csv the (hopefully) easier template to use: 2011_LHM_HW7_template.py.txt 
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 