Bayesian Belief Update Chain

validated scientific chain v1.0.0 cc-by-sa

Block ID: c81ede2a-12bd-4853-81dd-afe2510b7cbc

Community-contributed block. PromptDNA makes no guarantee of output quality or fitness for purpose. User assumes all responsibility for use.

Template

Update belief about the hypothesis in {hypothesis} given the new evidence in {evidence}, reasoning explicitly. (1) State the prior probability of the hypothesis and justify it from base rates or prior knowledge — not from the evidence you're about to consider, which would double-count. Make the prior a real number or defensible range, since a vague prior yields a vague conclusion. (2) State the likelihood: how probable is this evidence if the hypothesis is true? (3) State the crucial and often-neglected term: how probable is this same evidence if the hypothesis is false — evidence that's equally likely either way is worthless no matter how dramatic it seems. (4) Apply Bayes' theorem, showing the computation of the posterior from prior, likelihood, and the marginal probability of the evidence. (5) Interpret the shift: did the evidence move belief a little or a lot, and does the direction match intuition? A surprising update warrants rechecking the likelihoods. (6) State the posterior and what further evidence would most efficiently discriminate remaining uncertainty. Guard against confirmation bias by asking what evidence would have lowered the probability.

Update belief about the hypothesis in {hypothesis} given the new evidence in {evidence}, reasoning explicitly. (1) State the prior probability of the hypothesis and justify it from base rates or prior knowledge — not from the evidence you're about to consider, which would double-count. Make the prior a real number or defensible range, since a vague prior yields a vague conclusion. (2) State the likelihood: how probable is this evidence if the hypothesis is true? (3) State the crucial and often-neglected term: how probable is this same evidence if the hypothesis is false — evidence that's equally likely either way is worthless no matter how dramatic it seems. (4) Apply Bayes' theorem, showing the computation of the posterior from prior, likelihood, and the marginal probability of the evidence. (5) Interpret the shift: did the evidence move belief a little or a lot, and does the direction match intuition? A surprising update warrants rechecking the likelihoods. (6) State the posterior and what further evidence would most efficiently discriminate remaining uncertainty. Guard against confirmation bias by asking what evidence would have lowered the probability.

Variables

Name	Type	Required	Trust level
hypothesis		yes
evidence		yes

bayesian-reasoningprobabilitybelief-updatingchain-of-thought

Ratings

0.0

Overall (0)

0.0

Accuracy

0.0

Consistency

0.0

Clarity

0.0

Efficiency

Benchmarks

Not yet self-validated against any benchmark. Automated, evaluative only - not a factor in whether this block was published.

Submitted by James P FounderMod via mcp · 2026-07-18