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ProbEvidenceSampler 

Companion class ProbEvidenceSampler

object ProbEvidenceSampler

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4. def apply(baselineWaitingTime: Long, evidence: List[NamedEvidence[_]])(implicit universe: Universe)

Create an anytime sampler that computes probability of the named evidence.

Create an anytime sampler that computes probability of the named evidence. Takes the conditions and constraints in the model as part of the model definition. It also uses an anytime sampler for computing the baseline probability of conditions and constraints in the program.

baselineWaitingTime

The amount of time to allow the algorithm for computing the baseline probability to run.

5. def apply(numSamplesToUse: Int, evidence: List[NamedEvidence[_]])(implicit universe: Universe)

Create a one-time sampler that computes probability of the named evidence using the given number of samples.

Create a one-time sampler that computes probability of the named evidence using the given number of samples. Takes the conditions and constraints in the model as part of the model definition.

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8. def computeProbEvidence(waitingTime: Long, evidence: List[NamedEvidence[_]])(implicit universe: Universe): Double

Use anytime sampling to compute the probability of the given named evidence, taking the conditions and constraints in the model as part of the model definition.

Use anytime sampling to compute the probability of the given named evidence, taking the conditions and constraints in the model as part of the model definition. Takes the conditions and constraints in the model as part of the model definition. This method takes care of creating and running the necessary algorithms.

waitingTime

Total time given to all steps of the method.

9. def computeProbEvidence(numSamplesToUse: Int, evidence: List[NamedEvidence[_]])(implicit universe: Universe): Double

Use one-time sampling to compute the probability of the given named evidence.

Use one-time sampling to compute the probability of the given named evidence. Takes the conditions and constraints in the model as part of the model definition. This method takes care of creating and running the necessary algorithms.

10. val default: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double

Default algorithm to pass to dependent universe algorithms.

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