Forked Branch Probability

Combine explicitly independent genetic-cross branch probabilities with a finite log-probability output.

Description

Independent genetic-cross branches combine through multiplication.

Independent genetic-cross branches combine through multiplication. Log probability retains useful information when a small product underflows.

Combine explicitly independent genetic-cross branch probabilities with a finite log-probability output.

When to use Forked Branch Probability

Use this for the following task: Combine explicitly independent genetic-cross branch probabilities with a finite log-probability output.

Prepare the observed records or explicit model parameters requested below. Keep probabilities as fractions and paired records aligned.

How to use Forked Branch Probability and understand its results

Prepare the observed records or explicit model parameters requested below. Keep probabilities as fractions and paired records aligned.
Enter branchNames, branchProbabilities, independenceConfirmed using the field table, then run the tool.
Read jointProbability, logProbability. Interpret flags, units, ordering and placeholder values using the rules below.
Review any reported issue or failed check, correct the supplied values when appropriate, and verify the result in its intended context.

Input field	Meaning	Value type and bounds	Requirement
branchNames	Branch names	list of string; each entry: string	Required
branchProbabilities	Branch probabilities	list of number; each entry: number	Required
independenceConfirmed	Independence confirmed	boolean	Required

Align branch names with positive fractions no greater than one and require independenceConfirmed true. Sum natural logarithms and exponentiate.^[1]

logProbability=Σ ln(branchProbability); jointProbability=exp(logProbability)

This summarizes the declared operation. Units, indexing, inclusivity and exceptional cases are explained in the surrounding text.

Output field	Meaning	Value type
jointProbability	Joint probability	number
logProbability	Log probability	number

json

{
  "input": {
    "branchNames": [
      "A",
      "B",
      "C"
    ],
    "branchProbabilities": [
      0.5,
      0.25,
      0.5
    ],
    "independenceConfirmed": true
  },
  "output": {
    "jointProbability": 0.0625,
    "logProbability": -2.772588722239781
  }
}

Limitations and assumptions

Zero branch probability is rejected to keep log output finite. Independence is confirmed by the caller rather than tested.

The tool evaluates supplied observations or a declared model. It does not establish the biological cause of an observed pattern. Malformed requests produce input errors. A successfully evaluated request can still contain false flags, empty result lists or a result outside the supplied threshold; those outcomes have the meanings stated above.

Alternative or Complementary methods

Choose Weighted Gamete Grid for this operation: Build a weighted gamete-combination grid allowing caller-supplied transmission probabilities. For a complementary task, use Phenotype Class Aggregation for this operation: Aggregate genotype-class probabilities into caller-defined phenotype percentages.

The implementation reference documents the exact calculation, supported input shapes and returned fields.

References

Forked Branch Probability — tool.io

Similar or alternative tools

Weighted Gamete Grid
Build a weighted gamete-combination grid allowing caller-supplied transmission probabilities.
Phenotype Class Aggregation
Aggregate genotype-class probabilities into caller-defined phenotype percentages.