Likelihood methods in biology - Spring 2016

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.

Informatics tool

Text editor. TextWrangler
on Mac
Notepad++ on Windows
jEdit any platform with Java.

A free-of-charge 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.

Working from a shell

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 "Intro-to-Unix" section of the Woods Hole Workshop on Molecular Evolution's Computer lab introduction
if you have never worked from a shell before.

Slides

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: one-param-k2p-py.txt
2 param optimizer: k2p-py.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 high-level.py - the start point for class on Monday
work-in-progress.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 hw5-data.fas.txt
continuous-mcmc.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: model-jumping-mcmc.py. and hw5-answer.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) likelihood-hw6-mth-answer.py (not tuned at all, and only doing a sliding window proposal). And likelihood-hw6-mth-answer-opt-1.py (an optimized version that runs about 350 times faster on my machine.
mth-answer-opt-2.py
likelihood-hw7.pdf
model-jump-hastings-ratio.pdf