Protocol
Probability and Information for Models
Build a Bayesian diagnostic model for a synthetic noisy sensor. Compare two priors, specify the sampling process and explain the resulting posterior differences.
45–75 active hours60–90 min sessionsBeginner
Mastery contract
Core protocol · Standard 1.0 · Build a Bayesian diagnostic model for a synthetic noisy sensor. Compare two priors, specify the sampling process and explain the resulting posterior differences.
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Reasoning — not yet passed
Derive the 3 identities and give a counterexample to reversing conditional probabilities.
Reliable implementation — not yet passed
The Bayesian update normalizes and matches a hand calculation; log-space calculations remain finite on the stress case.
Reproducible experiment — not yet passed
Compare simulation estimates with a known analytic expectation over increasing sample sizes and recorded repeated seeds; retain errors and uncertainty, including nonmonotone individual runs.
Defense and handoff — not yet passed
Compute and plot predictive distributions under two stated priors on the same data; justify the uncertainty change without interpreting likelihood as posterior probability.
All four criteria must pass. Activity completion does not satisfy them.
An independent reviewer reruns your work and varies something you did not rehearse: a fresh input, a different working directory, or a declared edge case.
Pilot decisions are provisional, not external credentials. For an appeal or sensitive artifact, contact your designated pilot operator with the submission ID. Export your assessment history.
What you will do
6 total
Derive Bayes rule for a noisy binary test, Gaussian log likelihood, and cross-entropy as entropy plus KL; state the support assumptions.
Implement a discrete Bayesian update and a seeded Monte Carlo expectation estimator; compute log probabilities to avoid underflow.
Compare Monte Carlo estimates at 100, 1000 and 10000 samples over 20 seeds against an analytic expectation; report error distributions.
Debug a confusion between P(A|B) and P(B|A), and a product of tiny probabilities that becomes zero.
Write a 500–900 word technical report linking the reasoning, code, measurements, and limitations; include the project decision below.
Assess all 4 mastery criteria against saved artifacts, request an independent review, and repeat each failed criterion on a fresh example.
Essentials
A reading session alone never satisfies a mastery criterion.
Store source references, assumptions, and artifact paths beside every result.
Keep an untouched check case that differs from the worked example.
Record all attempts, including failures and results that contradict your prediction.
Use the stated comparison conditions; document every deviation before drawing a conclusion.
Protocol guardrails
Do
- +State the expected result before running the comparison.
- +Keep one minimal reproducible failing case when debugging.
- +Record environment versions and the exact command used.
- +Compare explanations with saved intermediate values.
- +Ask a reviewer to challenge the weakest assumption.
Don't
- ×Do not copy a worked solution and present it as an independent implementation.
- ×Do not tune against held-out evaluation outcomes.
- ×Do not report only the best seed or discard inconvenient runs.
- ×Do not equate elapsed hours or a completed run with a passed assessment.
- ×Do not conceal reduced-scale experiments behind claims about the original full-scale result.
Protocol authorship
Written by NuthinButta as instructional design. The teaching sources are the official references linked in each milestone; the exercises, workload and pass thresholds are ours, not their authors'.