forecasting-bayesian-updateslisted
Install: claude install-skill copyleftdev/superforecasting-skills-pack
# Updating Forecasts with Likelihood Ratios
Use the research-informed procedure below without claiming professional Superforecaster status or empirically demonstrated calibration.
## Workflow
1. Freeze the hypothesis, its complement, prior probability and information cutoff.
2. Specify the observed evidence precisely. Obtain P(E|H) and P(E|not H), or a likelihood ratio with an explicit basis.
3. Check source origin, selection effects, dependencies and whether the evidence was already incorporated into the prior.
4. Compute the posterior with Bayes' rule. Use the bundled helper for supplied numeric inputs.
5. For multiple signals, use likelihoods conditional on the evidence already incorporated; do not blindly multiply marginal likelihood ratios.
6. Report prior, evidence, likelihood assumptions, posterior, sensitivity and what remains unmodeled.
## Detailed resources
- Read [method](references/method.md) when applying this technique to a substantive task; it contains assumptions, equations and edge cases.
- Read [worked examples](references/examples.md) for a comparable case or to verify interpretation.
- Read [provenance](references/provenance.md) for original authors, source attribution and limits on evidence claims.