bayesian-reasoninglisted

# Bayesian Reasoning ## Concept Card **What it is:** Bayesian reasoning is a method for updating belief under uncertainty. It starts from a prior or base rate, evaluates how expected the new evidence is under competing hypotheses, updates toward the hypothesis that better predicts the evidence, and preserves residual uncertainty. **Mental model:** Confidence is not reset by each new clue. A belief has an existing level, evidence applies pressure to that level, and the posterior becomes the new prior for the next update. **Why it exists:** Agents tend to overreact to vivid recent evidence, ignore base rates, and answer uncertain questions as yes/no. Bayesian reasoning forces the belief state, evidence strength, and update size into the open. **What it is not:** It is not an expected-value decision table, a statistical modeling workflow, a generic prioritization method, a strategy framework, or a requirement to fabricate exact probabilities when inputs are weak. **Adjacent concepts:** base rates, priors, likelihood ratios, posterior probability, diagnostic reasoning, forecasting, calibration, expected value, hypothesis testing, evidence independence. **One-line analogy:** Bayesian reasoning is a confidence ledger: every new piece of evidence is posted against the prior balance before the new balance is reported. **Common misconception:** The method is not "new evidence says X, therefore X." Evidence matters by how differently it is predicted by X versus not-X, and by ho