|Fish are like trees, except they are invisible and they move: John Shepherd|| Most
and other wildlife professionals
who are tasked with providing scientific advice to decision makers
existing software tools insufficient for their needs, requiring the
development of purpose-built computer programs. The primary bases for
quantitative scientific advice are the results of statistical analyses
model-fitting exercises. The objective of this course is to
expose students who have taken courses in mathematical modeling and
statistical model fitting to numerical methods. Modeling
environments such as EXCELTM and Visual BasicTM
are sufficient for conducting several types of analyses, but more
powerful techniques are often needed to solve research questions. The
focus of this course will be on fisheries applications, but the models
and techniques to be covered are broadly applicable in
quantitative conservation biology.
The course is based on four major themes:
The numerical methods section of the course will introduce students to numerical differentiation and integration, interpolation and how to generate random numbers.
The R component of the course will focus on how to use R to
implement the numerical methods covered during the course, as well as
how to use R when fitting models using ADMB.
The WINBUGS component of the course will introduce students
to the use of the WINBUGS package for implementing Bayesian hierarchical models. The use of WINBUGS
will focus on how to conduct a meta-analysis.
Numerical techniques should not be applied "cookbook" style. It is necessary to understand the mathematical basis for the technique. Furthermore, numerical techniques frequently involve expert judgment regarding choices (e.g. for tolerances, initial values). The best way to learn a numerical technique therefore involves knowing its theoretical basis and spending time practicing it. This course will attempt to cover both aspects.
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