com.cra.figaro.experimental.marginalmap.ProbEvidenceMarginalMAP
MMAPProbEvidenceSampler
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package ProbEvidenceMarginalMAP
com.cra.figaro.experimental.marginalmap.ProbEvidenceMarginalMAP
Elements and corresponding values that should be observed each time this algorithm is run. Normally, this contains MAP elements and their proposed values.
The algorithm used to compute the probability of additional evidence, as created by probAdditionalEvidence.
The algorithm used to compute the probability of additional evidence, as created by probAdditionalEvidence. This algorithm can be different to the one defined in this class. (For example, a one-time algorithm can use an anytime algorithm for additional evidence.)
Removes the evidence provided in the constructor from the universe.
Removes the evidence provided in the constructor from the universe.
Perform sampling, but additionally update the variance and clear only elements that shouldn't be preserved.
Perform sampling, but additionally update the variance and clear only elements that shouldn't be preserved.
Since probability of evidence algorithms introduce additional evidence (namely, their evidence argument), into an existing universe, a mechanism must be provided for introducing the evidence when the algorithm begins and cleaning it up at the end.
Since probability of evidence algorithms introduce additional evidence (namely, their evidence argument), into an existing universe, a mechanism must be provided for introducing the evidence when the algorithm begins and cleaning it up at the end. This is achieved with the initialize method, called when the algorithm starts, and the cleanUp method, called when the algorithm is killed.
Kill the algorithm so that it is inactive.
Kill the algorithm so that it is inactive. It will no longer be able to provide answers.Throws AlgorithmInactiveException if the algorithm is not active.
The computed log probability of evidence.
The computed log probability of evidence.
The number of samples to collect from the model.
The number of samples to collect from the model.
Elements and corresponding values that should be observed each time this algorithm is run.
Elements and corresponding values that should be observed each time this algorithm is run. Normally, this contains MAP elements and their proposed values.
Returns an algorithm to compute the probability of the additional evidence provided.
Returns an algorithm to compute the probability of the additional evidence provided.
The computed probability of evidence.
The computed probability of evidence.
Returns the probability of evidence of the universe on which the algorithm operates.
Returns the probability of evidence of the universe on which the algorithm operates. Throws AlgorithmInactiveException if the algorithm is not active.
Record the weight in the rolling mean and variance computation.
Record the weight in the rolling mean and variance computation.
Log of the weight to record.
Resume the computation of the algorithm, if it has been stopped.
Resume the computation of the algorithm, if it has been stopped. Throws AlgorithmInactiveException if the algorithm is not active.
Observe the necessary values of MAP elements, then run the algorithm.
Observe the necessary values of MAP elements, then run the algorithm. After this is initialized, calling this method again is allowed. The additional samples are accounted for when returning the total log statistics.
Start the algorithm and make it active.
Start the algorithm and make it active. After it returns, the algorithm must be ready to provide answers. Throws AlgorithmActiveException if the algorithm is already active.
Stop the algorithm from computing.
Stop the algorithm from computing. The algorithm is still ready to provide answers after it returns. Throws AlgorithmInactiveException if the algorithm is not active.
Return the combined statistics for the log probability of evidence over all runs of this sampler.
Return the combined statistics for the log probability of evidence over all runs of this sampler. If the number of observations is 0, the returned log mean is -Infinity. If the number of observations is 0 or 1, the returned log variance is NaN.
Special probability of evidence sampler used for marginal MAP. Unlike a regular probability of evidence sampler, this records its own variance. It does so in an online fashion, and computes it in log space to prevent underflow. Additionally, this algorithm may be run multiple times. The rolling mean and variance computation incorporates the samples taken from all runs.