Posterior win probability models each optimization claim as a Beta distribution parameterised by \(\alpha\) and \(\beta\), where observed rank improvements act as successful Bernoulli trials to update the expected win rate for subsequent Thompson sampling.

flowchart TD
    subgraph Prior["Prior Belief"]
        P0["Beta(α₀, β₀)<br/>Flat / Initial Uncertainty"]
    end

    subgraph Trials["Observed Snippet Trials"]
        T1["Snippet vs Competitor Re-ranking"]
        S["Successes (k)<br/>Rank Improvement"]
        F["Failures (n - k)<br/>No Gain / Drop"]
    end

    subgraph Posterior["Posterior Win Probability"]
        P1["Beta(α₀ + k, β₀ + n - k)<br/>Peaked Win Rate Distribution"]
    end

    subgraph Decision["Next Iteration"]
        TS["Thompson Sampling<br/>Select Next Claim / Edit"]
    end

    P0 --> T1
    T1 --> S
    T1 --> F
    S --> P1
    F --> P1
    P1 --> TS
    TS -.->|Iterate| T1