Description
When erased is true, return priorOne.
Ideal binary crossover and erasure channels describe different forms of lost information. Their simple probability terms depend on assumptions about independence and error-free nonerased observations.
When erased is true, return priorOne.
When to use this tool
Use Binary Erasure Posterior Mass when you need one supplied ideal-channel component.
How to use the tool and how it works
- Enter priorOne, erased, observedOne. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read posteriorOne. Use the output convention below.
When erased is true, return priorOne. Otherwise return one for observedOne true and zero for observedOne false.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| priorOne | Prior One | Required. Finite number; minimum 0; maximum 1. |
| erased | Erased | Required. Boolean. |
| observedOne | Observed One | Required. Boolean. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| posteriorOne | Posterior One | Required. Finite number. |
{
"input": {
"priorOne": 0.3,
"erased": true,
"observedOne": false
},
"output": {
"posteriorOne": 0.3
}
}Limitations and assumptions
- This is an ideal binary erasure model without crossover errors; observedOne is ignored when erased.
- 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
Estimate the actual channel and evaluate complete input/output distributions before predicting transmission performance.
References
Similar or alternative tools
- Channel Erasure Output Mass Term
Split inputMass into inputMass × (1 - erasureProbability) retained and inputMass × erasureProbability erased.
- Information: Binary Channel Crossover Symmetry
Return one minus crossoverProbability and the smaller of the original and complementary probabilities.