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. The git repo for MTH's notes is at https://github.com/mtholder/likelihoodmethodscourse2019.
Date  Topic and Links  Assignments 

Week 1 Jan 23  Probability, random variables, distributions
Notes on probability by Bálint Tóth 
Homework 1: HW1_2019.doc data: PoissonCounts.xls 
Week 2 Jan 28, Jan 29  Random samples, sample distributions, likelihood.  
Week 3 Feb 04, Feb 06  Explicitly specifying variability: likelihood examples. notes on allele freq estimation 
Homework 2: HW2_2019.doc (frogs) 
Week 4 Feb 11, Feb 13  more Maximum likelihood estimation notes on population size estimation from sequences 

Week 5 Feb 18, Feb 20  Likelihood ratio test statistic and Model Selection notes on the counter CI vs Bayesian statement example Notes on Bayesian inference with conjugate priors 
Homework 3: HW3_2019.pdf Due Monday, 25 Feb. hw3answers.pdf 
Week 6 Feb 25, Feb 27  notes on two parameters and feasiblility constraints  Homework 4: HW4_2019.pdf 
Week 7 Mar 04, Mar 06  Likelihood ratio test statistic and Model Selection Some notes on Markov chains and their stationary distributions 

Week 8 Mar 18, Mar 20  Numerical optimization oneparamk2p.py example code  Homework 5: HW5_2019.doc data:Mutt_Gamete_data.xlsx 
Week 9 Mar 25, Mar 27  Fisher's Information and more code. oneparamk2p.R (one and two parameter version despite the name) and
k2p.py (two parameter version). mar272019.pdf notes 

Week 10 Apr 01, Apr 03  MCMC Notes on MCMC for Bayesian inference and the coin_contamination.py script HW4_2019_answer.pdf code MTH used to get numbers for key 
HW6_2019.pdf Due Wednesday, 17 Apr. 
Week 11 Apr 08, Apr 10  Markov chain Monte Carlo (MCMC) https://github.com/mtholder/likelihoodmethodscourse2019/blob/master/code/continuousmcmc.py 

Week 12 Apr 15, Apr 17  Hastings ratio and model jumping  
Week 13 Apr 22, Apr 24  Hastings ratio and model jumping See nice slides by Patrick Lam on convergence diagnostics at http://patricklam.org/teaching/convergence_print.pdf Some slides on Bayes' factors (ignore the irrelevant title slide) 
HW7_2019.pdf Due Monday, 6 May 
Week 14 Apr 29, May 01  Hidden Markov Models  
Week 15 May 06, May 08  HiSSE and imputation. See Gelman's chapter 