<commit_before>---
layout: page
title: likelihood and Bayesian approaches in Biology
permalink: /like-bayes-bio/
---

This site will have links to notes, slides, code and data sets used
in the "likelihood and Bayesian approaches in biology" hosted by
[Hellenic Centre for Marine Research](http://www.hcmr.gr) 15-18 June, 2015.

| Session | Link/Content |
|:------- |:-------------|
|Mon., lecture: Intro, Probability, Likelihood | [posted-day-1.zip](./posted-day-1.zip)|
|Tues., lecture: Model testing | |
|Wed., lecture: Bayesian, MCMC | |
|Thurs., lecture Model averaging MCMC | |

The source code for the slides and examples is at [https://github.com/mtholder/like-bayes-bio-4-day-workshop](https://github.com/mtholder/like-bayes-bio-4-day-workshop)

## Workshop goals
In the workshop we will cover the basics of using maximum likelihood and
Bayesian methods for inference. Basing inference on a likelihood function
is an extremely flexible and powerful framework for statistics.
Many of the "canned" statistical estimators and test statistics that are
taught in introductory statistics courses can be justified from the
perspective of maximum likelihood. But likelihood-based approaches can be 
tweaked to fit the specifics of complex experimental designs. So learning
the basic tools in the "likelihood toolbox" can be a very valuable research<commit_msg>fixed link<commit_after>---
layout: page
title: likelihood and Bayesian approaches in Biology
permalink: /like-bayes-bio/
---

This site will have links to notes, slides, code and data sets used
in the "likelihood and Bayesian approaches in biology" hosted by
[Hellenic Centre for Marine Research](http://www.hcmr.gr) 15-18 June, 2015.

| Session | Link/Content |
|:------- |:-------------|
|Mon., lecture: Intro, Probability, Likelihood | [posted-day-1.zip](http://phylo.bio.ku.edu/slides/posted-day-1.zip)|
|Tues., lecture: Model testing | |
|Wed., lecture: Bayesian, MCMC | |
|Thurs., lecture Model averaging MCMC | |

The source code for the slides and examples is at [https://github.com/mtholder/like-bayes-bio-4-day-workshop](https://github.com/mtholder/like-bayes-bio-4-day-workshop)

## Workshop goals
In the workshop we will cover the basics of using maximum likelihood and
Bayesian methods for inference. Basing inference on a likelihood function
is an extremely flexible and powerful framework for statistics.
Many of the "canned" statistical estimators and test statistics that are
taught in introductory statistics courses can be justified from the
perspective of maximum likelihood. But likelihood-based approaches can be 
tweaked to fit the specifics of complex experimental designs. So learning
the basic tools in the "likelihood toolbox" can be a very valuable research
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