Description
Return posterior-minus-prior and divide that gap by prior.
Evidence weights and credences are numerical representations under a chosen model. Likelihood ratios, probabilities, logarithmic weights and confidence bounds have different meanings; one local calculation does not establish the truth of a claim.
Return posterior-minus-prior and divide that gap by prior.
When to use this tool
Use Bayesian Confirmation Prior Posterior Gap when you need an explicit evidence-weight or probability-consistency term.
How to use the tool and how it works
- Enter prior, posterior. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read difference, relativeIncrease. Use the output convention below.
Require prior in (0, 1] and posterior in [0, 1]. Return posterior-minus-prior and divide that gap by prior.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| prior | Prior | Required. Finite number; maximum 1; strictly above 0. |
| posterior | Posterior | Required. Finite number; minimum 0; maximum 1. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| difference | Difference | Required. Finite number. |
| relativeIncrease | Relative Increase | Required. Finite number. |
{
"input": {
"prior": 0.2,
"posterior": 0.5
},
"output": {
"difference": 0.3,
"relativeIncrease": 1.4999999999999998
}
}Limitations and assumptions
- These are supplied belief changes, not Bayes-factor estimation or posterior computation. Relative increase is a fraction, not percent, and prior zero is excluded.
- Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
- Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Numeric signed-zero results are exported as zero.
Alternative or Complementary methods
Evaluate source quality and the complete inference model. Check likelihood meaning, dependence, prior assumptions, normalization and uncertainty rather than substituting counts or weights for justified evidence.
References
Similar or alternative tools
- Credence Log Odds One Update Term
Add priorLogOdds and logLikelihoodRatio.
- Likelihood Evidence Direction One Pair
Divide true/false likelihood and label supports, opposes or neutral by their ordering.