Workshops‎ > ‎

Likelihood Methods

Monday, March 19, 2012  14h-16h, Stewart Biology N4/17
Corey Chivers, Department of Biology

Topics

This workshop will introduce participants to the likelihood principal and its utility in statistical inference.  By learning how to formalize models of biological processes through their likelihood function, participants will learn how to confront these models with data in order to make statistical inference.

The concepts of using maximum likelihood to fit model parameters, and model comparison using information theoretic approaches will also be covered. 

The workshop will explore these topics through worked examples and exercises using the R statistical computing environment.

This is the first official meetup of the Montreal R User Group. Be sure to join the group and RSVP.

Learning Objectives

The participant will:

1) Formalize the evidence about hypotheses that is contained in data through the likelihood function.
2) Fit model parameters using maximum likelihood estimation (MLE).
3) Extend MLE to compare between competing models using information theory (AIC).

Prerequisites

The goal of this workshop is to demystify these potentially 'scary' topics, and empower participants (of any preexisting knowledge level) to engage in statistical reasoning when conducting their own research.  So come one, come all!

That being said, a basic working understanding of R is assumed.  Knowledge of functions and loops in R will be advantageous, but not a must.

R Packages

install.packages('emdbook')

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Corey Chivers,
Mar 19, 2012, 9:28 AM
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likelihood_BGSA_WS.R
(9k)
Corey Chivers,
Mar 19, 2012, 8:55 AM
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