CEF-Formation - Likelihood Methods in Ecology
Responsable de la formation: Michael Papaik
The CEF is offering a three-day intensive short course to introduce students, post-docs and the CEF community in the use of likelihood methods in ecology. The course is designed to be a conceptual and hands-on introduction to the use of maximum likelihood analysis in forest ecology.
In addition to preparing the student for designing their own likelihood analyses, this course is designed to provide the prerequisites for the full 2-week course offered by The Institute of Ecosystem Studies. The full intensive, 2-week course covers the concepts, theories, and application of likelihood methods as a comprehensive framework for analysis of ecological data and for testing alternate hypotheses in the context of model comparison.
The CEF course will primarily be discussion based with topics including likelihood principles, probability and examples from recent literature. Students will be expected to have read the course readings before the first class meeting and actively participate in discussions. There will also be seminars on applications in ecology and computer-based labs in which students use the R software package to learn likelihood methods and modeling. The advanced course at IES offers a more extensive and intensive exposure to the methods and applications in ecology as well as substantial lab time devoted to independent projects for which students are encouraged to bring their own data for analysis.
Location: Pavillon Président-Kennedy (UQAM)
15 students maximum | Register with Michael Papaik
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.
- Provide an overview of a likelihood framework for developing and evaluating models (as hypotheses) based on the strength of evidence provided by the data. Discussions will include the pros and cons of the likelihood approach as an alternative to frequentist statistics.
- Give students the knowledge, skill, and confidence to use likelihood methods to enhance their research.
Format and Approach
Morning discussions of theory and applications from 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 comprehensive intensive course offered at The Institute of Ecosystem Studies.
Afternoon lab sessions will challenge students to build and parameterize models using likelihood methods. Lab exercises will be given to complement the material presented in morning discussions. All of the labs and independent projects are done using the R package for statistical computing.
Privacy Statement: The information you provide will not be shared with anyone outside of the CEF or the Institute for Ecosystem Studies and shall be used for no purpose other than that stated herein. Disclaimer: This material is based, in part, upon work supported by the National Science Foundation under Grant No. 0087214 and the CEF. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the CEF.
Introduction to Likelihood Methods
Maximum likelihood methods have a long history of use for point and interval estimation in statistics. In contrast, likelihood principles have only more gradually emerged as a foundation for an alternative to traditional hypothesis testing via frequentist test statistics. The alternative framework stresses the use of likelihood and information theory as the basis for parameterizing and selecting among competing models, or in the simplest case, among competing point estimates of a parameter of a model. In contrast to traditional approaches, in which the statistical models are often constrained by the choice of a particular test statistic, a likelihood framework stresses the specification of both the "scientific" model that embodies the hypotheses and relationships to be tested, and the appropriate "probability" model that characterizes the statistical properties of the data and the error structure.
There are 4 general steps involved in a likelihood analysis:
- model specification including both alternate scientific models and appropriate error structures
- maximum likelihood parameter estimation using optimization methods
- model comparison using information theory, and
- model evaluation using a variety of metrics of precision, bias, and goodness of fit.
La formation sera donnée en anglais.
Les participants à la formation peuvent amener leur portable avec R (version 2.6.0) préalablement installé. Pour télécharger et installer ces logiciels sur votre ordinateur, cliquez ici.
Les étudiants pourront également utiliser les ordinateurs de la salle de formation, mais devront connaître leur code d’accès MS pour s’authentifier sur les ordinateurs du LAMISS.