Bayesian Entropy Tutor & LaTeX Guidance

I’m preparing a paper that applies Bayesian estimation to an entropy-based measure under upper record ranked set sampling scheme and I’d like a mentor to walk me through the full workflow— from sharpening the underlying theory right through to clean LaTeX write-up.

Where I stand
I already have some exposure to Bayesian analysis, but this project pushes me further. I understand the general ideas; what I’m missing is confidence in implementing the estimator correctly and presenting the results with the rigour journals expect.

Scope of the help I need
• Deepen my grasp of the theoretical setup so every assumption is explicit.
• Translate that theory into code (R, Python + PyMC / Stan—whichever you’re most comfortable demonstrating).
• Debug and validate the entropy estimator so the output makes statistical sense.
• Shape the narrative into polished LaTeX: tidy equations, well-formatted tables/figures, and BibTeX management.

How we’ll work
I picture structured live sessions (screen-share or recorded walkthroughs) plus annotated notebooks and LaTeX snippets I can reuse. I’ll arrive with any data sets, drafts, or half-written code so we can iterate quickly.

Acceptance
By the end I should have:
1. A reproducible script or notebook that runs the Bayesian entropy estimation end-to-end.
2. A LaTeX section—methods, results, and figures—that’s ready for peer review.
3. Clear explanations I can reference when defending the choices during submission.

If this tutoring style and content match your expertise, let’s schedule our first session.