gov.sandia.cognition.statistics.method
Class ImportanceSampling
java.lang.Object
gov.sandia.cognition.statistics.method.ImportanceSampling
@PublicationReference(author="Wikipedia",
title="Importance Sampling",
type=WebPage,
year=2009,
url="http://en.wikipedia.org/wiki/Importance_sampling")
public class ImportanceSampling
 extends Object
Importance sampling is a technique for estimating properties of
a target distribution, while only having samples generated from an
"importance" distribution rather than the target distribution.
Typically, the importance distribution is easy to sample from, while the
target distribution is difficult to sample from, and the importance
distribution has support everywhere that the target distribution has
support. Then, this results in an weighted set of samples
that are an unbiased sampling of the target distribution.
 Since:
 3.0
 Author:
 Kevin R. Dixon
Method Summary 
static

sample(ProbabilityDensityFunction<ValueType> importanceDistribution,
Evaluator<ValueType,Double> targetDistribution,
Random random,
int numSamples)
Importance sampling is a technique for estimating properties of
a target distribution, while only having samples generated from an
"importance" distribution rather than the target distribution. 
Methods inherited from class java.lang.Object 
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait 
ImportanceSampling
public ImportanceSampling()
sample
public static <ValueType> ArrayList<DefaultWeightedValue<ValueType>> sample(ProbabilityDensityFunction<ValueType> importanceDistribution,
Evaluator<ValueType,Double> targetDistribution,
Random random,
int numSamples)
 Importance sampling is a technique for estimating properties of
a target distribution, while only having samples generated from an
"importance" distribution rather than the target distribution.
Typically, the importance distribution is easy to sample from, while the
target distribution is difficult to sample from, and the importance
distribution has support everywhere that the target distribution has
support. Then, this results in an weighted set of samples
that are an unbiased sampling of the target distribution.
 Type Parameters:
ValueType
 Domain type of the distributions. Parameters:
importanceDistribution
 Easytosamplefrom distribution that will generate the samples.targetDistribution
 The hardtosamplefrom distribution that is desired.random
 Random number generator.numSamples
 Number of samples to create.
 Returns:
 Weighted samples that are an unbiased estimate of the target
distribution.