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General Objectives
- Provide an overview of a
likelihood framework for developing and evaluating models (as
hypotheses) based on the strength of evidence provided by the data,
as an alternative to both traditional frequentist statistics and
Bayesian methods.
- Give students the knowledge,
skill, and confidence to use likelihood methods to enhance their
research.
Format and Approach
Daily lectures present
principles, while seminars present specific examples from ecological
research. These are supplemented by recommended readings from the
statistical and ecological literature. See the Course
Schedule for details from the most recent course.
Daily lab sessions
challenge students to build and parameterize models using likelihood
methods. The labs are the most important part of the course - our
experience is that students only learn the methods through practice. All
of the labs are done using the R package for statistical computing.
Prerequisites and
Intended Audience
The course is intended
for graduate students, post-docs, and practicing scientists. An
undergraduate or graduate level background in statistics is desired, but
the course will teach the basic principles of probability theory required
for the methods. Experience with R is useful, but basic skills in R are
taught throughout the labs.
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