definesProbability
P12745
predicate
Indicates that one entity specifies or assigns the probability value associated with another entity or event.
All labels observed (18)
| Label | Occurrences |
|---|---|
| probabilityFormula | 2 |
| probabilityRule | 2 |
| acceptanceProbabilityForSymmetricProposal | 1 |
| definesProbability canonical | 1 |
| edgeProbability | 1 |
| hasProbabilisticInterpretation | 1 |
| predictsProbabilityOfLeadingDigit1 | 1 |
| predictsProbabilityOfLeadingDigit2 | 1 |
| predictsProbabilityOfLeadingDigit3 | 1 |
| predictsProbabilityOfLeadingDigit4 | 1 |
| predictsProbabilityOfLeadingDigit6 | 1 |
| predictsProbabilityOfLeadingDigit7 | 1 |
| predictsProbabilityOfLeadingDigit8 | 1 |
| predictsProbabilityOfLeadingDigit9 | 1 |
| probabilisticOutput | 1 |
| probabilityModel | 1 |
| probabilityRelation | 1 |
| supportsProbabilityEstimates | 1 |
Description generation (PDg)
The one-sentence description above was generated by prompting gpt-5.1 with the predicate name and this instruction.
Instruction
Given a predicate that represents a relationship or action between entities, generate a one-sentence description explaining its meaning. # Instructions Focus on describing the relationship, not the entities themselves. # Response Format Begin the description with \' Indicates...\'
Input
Predicate: definesProbability
Generated description
Indicates that one entity specifies or assigns the probability value associated with another entity or event.
Sample triples (20)
| Subject | Object |
|---|---|
| Boltzmann machines | P(s) = exp(-E(s))/Z ⓘ |
| S-matrix | P_{i→f} = |S_{fi}|^2 via predicate surface "probabilityRelation" ⓘ |
| LogisticRegression | True via predicate surface "supportsProbabilityEstimates" ⓘ |
| SVC | predict_proba via predicate surface "probabilisticOutput" ⓘ |
| Newcomb–Benford law | approximately 0.301 via predicate surface "predictsProbabilityOfLeadingDigit1" ⓘ |
| Newcomb–Benford law | approximately 0.176 via predicate surface "predictsProbabilityOfLeadingDigit2" ⓘ |
| Newcomb–Benford law | approximately 0.125 via predicate surface "predictsProbabilityOfLeadingDigit3" ⓘ |
| Newcomb–Benford law | approximately 0.097 via predicate surface "predictsProbabilityOfLeadingDigit4" ⓘ |
| Newcomb–Benford law | approximately 0.067 via predicate surface "predictsProbabilityOfLeadingDigit6" ⓘ |
| Newcomb–Benford law | approximately 0.058 via predicate surface "predictsProbabilityOfLeadingDigit7" ⓘ |
| Newcomb–Benford law | approximately 0.051 via predicate surface "predictsProbabilityOfLeadingDigit8" ⓘ |
| Newcomb–Benford law | approximately 0.046 via predicate surface "predictsProbabilityOfLeadingDigit9" ⓘ |
| WaveGlow | exact likelihood model via predicate surface "probabilityModel" ⓘ |
| Erdős–Rényi model | p via predicate surface "edgeProbability" ⓘ |
| Metropolis algorithm | min(1, π(x') / π(x)) via predicate surface "acceptanceProbabilityForSymmetricProposal" ⓘ |
| Bernstein polynomials | B_{n,k}(x) as probability of k successes in n Bernoulli trials with parameter x via predicate surface "hasProbabilisticInterpretation" ⓘ |
| Chinese restaurant process | probability of joining existing table k is proportional to its current number of customers via predicate surface "probabilityRule" ⓘ |
| Chinese restaurant process | probability of starting a new table is proportional to alpha via predicate surface "probabilityRule" ⓘ |
| Chinese restaurant process | P(join table k) = n_k / (n + α) via predicate surface "probabilityFormula" ⓘ |
| Chinese restaurant process | P(new table) = α / (n + α) via predicate surface "probabilityFormula" ⓘ |