Rubric

Applied Bayesian Modeling for Natural Resource Management Project Rubric

Total points remaining: 45

Presentation points: 22.5

Write up points: 22.5

The 22.5 points will be weighted equally among the criteria in the first column, with both the project presentations and write-ups judged by the same criteria:

Criteria	Excellent	Proficient	Basic	Below Expectations
Content	Demonstrates a strong understanding of Bayesian concepts, with a comprehensive and well-supported application of statistical methods.	Conveys a satisfactory grasp of Bayesian principles with clear and well-supported statistical content.	Provides basic application of Bayesian concepts but may lack depth or thorough support.	Contains inaccurate or incomplete use of Bayesian statistics.
Clarity	Presents ideas and statistical results in a clear, organized, and easily understandable manner, with effective communication of probabilistic concepts.	Communicates effectively with clarity, but may have occasional instances of confusion in conveying concepts.	Contains some unclear or confusing elements, making it challenging to follow reasoning.	Lacks coherence in presenting concepts, making understanding difficult.
Methodology	Clearly outlines a well-thought-out and robust Bayesian methodology, demonstrating a systematic and appropriate approach to statistical modeling and inference.	Provides a sound Bayesian methodology, but some aspects may be underdeveloped or unclear in terms of modeling and inference.	Includes a basic methodology, but lacks detail or a clear plan for statistical modeling.	Methodology is unclear, incomplete, or significantly flawed in terms of Bayesian principles.
Model Evaluation	Demonstrates a comprehensive and effective evaluation of Bayesian model outcomes, using appropriate methods and justification of model components (like the choice of priors and likelihoods).	Conducts a thorough evaluation of Bayesian results, but some aspects may be less detailed or miss key points in justifying model choices.	Conducts a basic evaluation but lacks depth or may not cover all relevant aspects of model assessment.	Evaluation is unclear, incomplete, or does not align with the project’s objectives.