bayesian-world-model

Bayesian belief updating combines prior confidence with fresh evidence to calculate a posterior likelihood. The Skill classifies observed signals from repo status or APIs, compares evidence quality, and adjusts confidence thresholds to output promote, hold, reject, or repoll decisions.

Tracking confidence requires maintaining a belief ledger of priors and evidence classes. The Skill processes live polling signals, evaluates likelihoods against current beliefs, and records uncertainty to update posterior confidence for each active claim.

Yes, Bayesian confidence tracking applies to live API checks and device state monitoring. The Skill evaluates evidence from these sources, compares it against prior beliefs, and updates structured confidence decisions for monitored claims.

Updating decisions from observed signals involves classifying evidence and comparing likelihoods. The Skill records uncertainty, weighs fresh evidence against priors, and determines whether to promote, hold, reject, or repoll based on the updated posterior confidence.

Choose a Bayesian belief ledger when polling workflows involve incomplete or shifting information. The Skill structures observations into explicit confidence updates with tracked uncertainty, whereas standard polling lacks formal posterior likelihood comparison and evidence classification.