Likelihood methods in biology - Spring 2019
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):
| John Kelly | jkk@ku.edu | 864-3706 | 5005 Haworth |
| Mark Holder | mtholder@ku.edu | 864-5789 | 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/likelihood-methods-course-2019.
Slides
| 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. hw3-answers.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 one-param-k2p.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. one-param-k2p.R (one and two parameter version despite the name) and
k2p.py (two parameter version). mar-27-2019.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/likelihood-methods-course-2019/blob/master/code/continuous-mcmc.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 |