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Final Project Guidelines: Bayesian Methods

Final Project: Bayesian Methods

Overview

The final project for this course is a research paper that demonstrates your ability to apply, extend, and communicate ideas in Bayesian statistics. This project is intended as the culmination of your work in the class and should reflect a deep engagement with a Bayesian topic of your choosing.

You may choose one of the following directions (or propose something different, pending instructor approval):

• Model-focused: Investigate a specific Bayesian model or class of models (e.g., hierarchical models, Gaussian processes, Bayesian nonparametrics).

• Computation-focused: Explore a Bayesian computational technique (e.g., MCMC variants, variational inference, sequential Monte Carlo) and demonstrate its use.

• Application-focused: Apply Bayesian methods to a substantive research question using real or simulated data.

• Theory-focused: Analyze a methodological problem in Bayesian inference and provide theoretical insights.

The project should be research-level in tone, but you are not expected to produce publishable work. Instead, aim for a paper that could serve as a foundation for future thesis/dissertation research, or that demonstrates mastery of key Bayesian ideas.

Deliverable

A written paper in the style of a statistics research article.

• Length: ~12—18 pages (double-spaced, 11—12 pt font, not counting references or appendices).

• Format: Professional typesetting (LaTeX strongly encouraged).

• Due: The day of the university-scheduled final exam (no in-class exam).

• Submission: PDF via Gradescope.

Suggested Paper Structure

While you have some flexibility, your paper should follow the general structure of scholarly articles in Bayesian statistics:

1. Abstract ( fewer than 250 words)

• Briefly state the problem, the Bayesian approach you used, main findings, and contributions.

2. Introduction

• Provide background and motivation.

• State the problem/question clearly.

• Situate your work in the literature (with citations).

• Outline the structure of your paper.

3. Background / Literature Review

• Summarize prior work relevant to your project.

• Explain why Bayesian methods are useful in this context.

• Clarify what gap, extension, or application your project addresses.

4. Methods

• Define the model(s), prior(s), likelihood, and inferential approach.

• Include mathematical formulations where appropriate.

• If computational, describe algorithms (with pseudocode if needed).

• Justify choices of priors, modeling assumptions, and computational methods.

5. Results / Case Study

• Present results of simulations or applied data analysis.

• Include figures, tables, and diagnostics.

• Interpret results in the context of the problem.

6. Discussion

• Reflect on findings, strengths, and limitations.

• Suggest potential extensions or open questions.

7. Conclusion

• Provide a concise summary of contributions and insights.

8. References

• Use a consistent citation style (APA, ASA, or similar).

• Cite relevant statistical and applied literature appropriately.

9. Appendices (optional)

• Code, additional proofs, or extended figures/tables.

Style & Expectations

• Clarity: Write for a statistically literate audience (your classmates and faculty).

• Professionalism: Use proper academic tone, grammar, and structure.

• Figures/Tables: All graphics should be labeled, numbered, and discussed in the text.

• Reproducibility: Code should be available (appendix or separate file).

• Citations: Use BibTeX or similar to manage references; cite journal articles, textbooks, or software appropriately.

• Originality: Projects should be your own work. External sources must be cited.

Grading Rubric (35% of course grade)

Final Project: Bayesian Methods