Bayesian Product Discovery — two-level inference demo

Static, interactive page: prior project success belief, posterior after five problem interviews, delivery capability for a four-week production slice, and overall belief.

Run locally

python3 -m http.server 8080

Open http://127.0.0.1:8080/.

GitHub Pages (free hosting)

1. Push this repo to GitHub.
2. Go to Settings → Pages → Build and deployment.
3. Set Source to Deploy from a branch.
4. Set branch to main and folder to / (root).
5. The site URL will be https://<user>.github.io/<repo>/.

GitHub Pages only serves static files; the chart runs in the browser with Plotly.js.

What the demo shows

The demo is for teaching Bayesian product discovery in a simple product-investment conversation:

1. Start with an outside-view prior belief about project success.
2. Update that belief after five users independently cite the same problem as important.
3. Discount or strengthen confidence based on delivery capability: can the team ship a thin slice to production within four weeks?

The final number is:

overall belief = P(success after problem evidence) × P(deliver a production slice in 4 weeks)

Repository structure

.
├── index.html
├── README.md
├── data/
└── scripts/

The data/ and scripts/ folders are present to match the structure of the LLN demo and leave room for future generated examples.

Licence

Open source — add a LICENSE file of your choice, for example MIT.