Course ID: | WILD(FISH) 8370-8370L. 3 hours. 2 hours lecture and 2 hours lab per week. |
Course Title: | Bayesian Modeling for Conservation Science |
Course Description: | Concepts and practices of Bayesian modeling for problem solving
in natural resources management. Comparison to frequentist
approaches, with Bayesian analogues presented. Demonstration of
usefulness of Bayesian approach to analysis of complex study
designs, with emphasis on plant, insect, fish, and wildlife
populations. |
Oasis Title: | Bayesian Models for Consrv Sci |
Semester Course Offered: | Offered spring semester every odd-numbered year. |
Grading System: | A-F (Traditional) |
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Course Objectives: | The goals of the course are to instill a basic understanding of
and appreciation for Bayesian principles for the solution of
problems in natural resources science; to gain practical
experience in constructing, evaluating, and interpreting models
under the Bayesian framework; and to explore classes of models
useful for conservation science. After completion of this
course, students will be able to design and build models
suitable for addressing problems in their own research, write
and execute programming code to analyze these models, and
generalize concepts sufficiently to address future applications
in their careers. |
Topical Outline: | 1. Foundations – philosophy, probability, Bayes’ Theorem,
Bayesian and frequentist approaches, common distributions,
conjugacy
2. Solutions – analytical and computational approaches, software
3. Diagnostics – convergence, model fit, model comparison
4. Application to problems familiar (e.g., t-test, ANOVA,
regression) and not so familiar
5. Hierarchical models
6. Models for marked populations (animal, plant, human)
7. Models for unmarked populations
8. State-space models
9. Spatial models |
Honor Code Reference: | Students in this course must comply with the University Honor
Code and Academic Honesty Policy. Students may discuss homework
assignments for clarification but any completed assignment/test
must represent the work of a single individual. Group projects
will be explicitly identified in class and will represent the
collaborative efforts of the individuals within the group.
Cheating on assignments/tests will be handled as described in
the Academic Honesty Policy Handbook. |