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Bayesian reasoning: beliefs are probabilities; evidence updates them proportionally.
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Based on SOC occupation classification
| name | bayesian |
| type | skill |
| hint | Bayesian updating — priors + evidence = posterior; update beliefs proportionally to evidence strength |
| version | 1.0.0 |
| tags | ["transferable","bayesian","prior","posterior","evidence","probability","update","inference","belief"] |
| tier | ch0nky |
| complexity | 5 |
| description | Bayesian reasoning: beliefs are probabilities; evidence updates them proportionally. |
Bayesian reasoning treats beliefs as probabilities that are updated proportionally as new evidence arrives. The core formula — P(H|E) = P(E|H) × P(H) / P(E) — in plain terms means: posterior = likelihood × prior / normalizer. You start with a prior (what you believed before), observe evidence, and update. Strong evidence produces large updates; weak evidence produces small updates; confirming noise produces tiny updates.
The key insight is that you must have a prior. "I have no opinion" is still a 50/50 prior — state it explicitly. A common error is base rate neglect — ignoring P(H) and overweighting P(E|H). In debugging, ask "what is my prior that this module has a bug vs that module?" and start there. In decision-making, each new data point is an update, not a verdict.
Apply Bayesian reasoning whenever you need to update a belief based on new information, assess probabilities under uncertainty, or avoid base rate neglect. Use it in debugging triage, risk assessment, and any situation where evidence quality varies.
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