phylo.bio.ku.edu

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.

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 notes on the HR of the model jumping move.