Proof of Concept / Bayesian Decision Science
Adaptive Campaign Optimization for IgA Nephropathy
A Thompson Sampling engine for optimizing pharma digital marketing campaigns targeting nephrologists. This proof of concept demonstrates how Bayesian inference adaptively allocates campaign budget across creative variants in real time — maximizing HCP engagement while minimizing wasted impressions on underperforming content. Built entirely in-browser with no backend dependencies.
Disclaimer: This is a proof-of-concept demonstration using synthetic data and simulated campaign parameters. All conversion rates, budget figures, and campaign configurations are illustrative and do not represent actual campaign data, clinical outcomes, or commercial strategies of Travere Therapeutics or any other entity. This tool is NOT intended to guide actual marketing spend decisions.
Running Thompson sampling simulation...
Beta-Binomial conjugate model with closed-form posterior updates. Each variant maintains a Beta(alpha, beta) posterior that updates incrementally with each observed conversion or non-conversion. Log-space computation via Lanczos approximation ensures numerical stability for large alpha/beta values.
Adaptive allocation algorithm that balances exploration and exploitation. Each round, the engine samples from each variant's posterior and allocates the next impression to the variant with the highest sample. Seeded Mulberry32 PRNG ensures full reproducibility across simulation runs.
Expected loss and probability of being best computed via 10,000 Monte Carlo samples from the joint posterior. Dual decision threshold (P(best) > 95% AND expected loss < 0.1%) ensures both statistical confidence and practical significance before declaring a winner.