Bayesian Posterior Update Mechanics — DEJAN Codebase Explorer

Each edit hypothesis is modeled as a Beta(α, β) distribution updated by a Bernoulli win-loss trial based on whether the re-ranker moved the target URL up against its competitors.

flowchart TD
    A["Prior Belief<br>Beta(α, β)"] --> B["Apply Content Edit"]
    B --> C{"Ranker Evaluation<br>Rank Improved?"}
    C -- "Yes" --> D["Success Reward<br>r = 1"]
    C -- "No" --> E["Failure Reward<br>r = 0"]
    D --> F["Updated Posterior<br>Beta(α + r, β + 1 - r)"]
    E --> F
    F -. "Next Iteration" .-> A

Referenced by

Bayesian Optimization Testing Loop
Bayesian testing loop mechanics
Bayesian Acquisition Policy Mechanics
Content Optimizer Factor Evaluation
Exploration vs Exploitation Balancing
Bayesian Optimization Engine Mechanics
Stage 2 Bayesian Acquisition
Content Optimizer Factor Evaluation
Testable Claim Definition
Rank Factor Taxonomy & Custodian
Bayesian Optimization Loop Deep Dive
Ranker Rationales in Optimization
Bayesian Optimization Loop Mechanics
Rank Factor Hypotheses
Beta distribution claim updates
Bayesian Ranker Scoring Role
Bayesian Optimization Loop Mechanics
Ranker Rationale Feedback Loop