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):
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.
Text editor. TextWrangler
on Mac
Notepad++ on Windows
jEdit any platform with Java.
A freeofcharge installation of python with the packages that we will use are available from
Anaconda from Continuum Analytics.
RStudio is useful if you prefer to use R.
There are some difference between a DOS prompt and UNIX. For the simple stuff, the main difference is dir
on Windows instead of ls
on Unix.
You probably should check out the "IntrotoUnix" section of the Woods Hole Workshop on Molecular Evolution's Computer lab introduction
if you have never worked from a shell before.
on convergence diagnostics.
notes on the HR of the model jumping move.
Date  Topic and Links  Assignments 

Week 1 Jan 20  Probability, random variables, distributions Notes on probability by Bálint Tóth. Some of MTH's notes on JKK's lecture 

Week 2 Jan 25, Jan 27  Random samples, sample distributions, likelihood. Some of MTH's lec2.pdf and lec3.pdf on JKK's lectures  HW 1 (due Wed., Feb 3). A (corrected) spreadsheet with the data for the birds is available as a zip archive: Jack.ods (or Jack.xls). You will also need PoissonCounts.xls Laura's as a HW1_Laura.pdf 
Week 3 Feb 01, Feb 03  Explicitly specifying variability: likelihood examples. MTH's lec4.pdf and lec5.pdf notes. JKK's saxony.ppt  SimpleLobsters.xls HW2_2016.doc revised (due Wed. Feb. 17) 
Week 4 Feb 08, Feb 10  Maximum likelihood estimation and Markov chains. MTH's lecture 6 notes and lecture 7 notes  
Week 5 Feb 15, Feb 17.  Likelihood ratio test statistic and Model Selection lec8.pdf twoParamNormal.pdf  HW3_2016.doc hw3.birds.1.txt bird_cruncher.py.txt mth_bird_cruncher.py.txt 
Week 6 Feb 22, Feb 24  Generalized Linear Models (continued)  
Week 7 Feb 29, Mar 04  Likelihood ratio test statistic and Model Selection  numerical optimization notes 1 param optimizer: oneparamk2ppy.txt 2 param optimizer: k2ppy.txt 
Week 8 Mar 07, Mar 09  Logistic regression  JKK's code for logistic regression data for JKK's code MTH version with parametric bootstrapping. 
Week 9 Mar 21, Mar 23  Bayesian methods  hw4.pdf Notes on MCMC for Bayesian inference and the coin_contamination.py script 
Week 10 Mar 28, Mar 30  Computational stuff  highlevel.py  the start point for class on Monday workinprogress.py script that I worked on in class. 
Week 11 Apr 04, Apr 06  Computational aspects: Markov chain Monte Carlo (MCMC) hastingsRatio.pdf  hw5.pdf, hw5template.py and hw5data.fas.txt continuousmcmc.py and diagnostics.R and summarize.R. See Patrick Lam's very nice slides 
Week 12 Apr 11, Apr 13  Multiparameter MCMC  MCMCMC version of the continuous MCMC example 
Week 13 Apr 18, Apr 20  Hastings ratio and model jumping  hw6.pdf (with links in that doc) and Bayes Factor slides Green's hastings ratio recipe. Wed: modeljumpingmcmc.py. and hw5answer.py 
Week 14 Apr 25, Apr 27  HMM  JKK's hmm1.py and hmm2.py 
Week 15 May 02, May 04  Finishing model jumping. Ascertainment bias (maybe)  likelihoodhw6mthanswer.py (not tuned at all, and only doing a sliding window proposal). And likelihoodhw6mthansweropt1.py (an optimized version that runs about 350 times faster on my machine. mthansweropt2.py likelihoodhw7.pdf modeljumphastingsratio.pdf 