Class EmpiricalDistribution

  extended bycern.colt.PersistentObject
      extended byhep.aida.bin.AbstractBin
          extended byhep.aida.bin.AbstractBin1D
              extended byhep.aida.bin.StaticBin1D
                  extended byhep.aida.bin.MightyStaticBin1D
                      extended byhep.aida.bin.QuantileBin1D
                          extended byhep.aida.bin.DynamicBin1D
                              extended byStatistics.EmpiricalDistribution
All Implemented Interfaces:
java.lang.Cloneable, cern.colt.buffer.DoubleBufferConsumer,

public class EmpiricalDistribution
extends hep.aida.bin.DynamicBin1D

Container for data implementing a large number of efficient statistics.

This class links us with the colt library. It extends the class hep.aida.bin.DynamicBin1D which is the most capable data container of the hep.aida.bin package in the colt distribution (quantiles, cumulative distribution functions).

Information: An empirical distribution is constructed from a sample of values of a random variable. Random variables X are often observed in a context with increasing information becoming available about X as time t passes. Clearly one will draw from the distribution of X by conditioning on this information.

Exactly what information is available at time t and how to condition on it is context specific and depends on the random variable. If no information about X is available the parameter t is simply not used and so unconditional statistics computed.

Memory: since all sample data are stored internally an empirical distribution can consume large amounts of memory depending on the sample size.

See Also:
Serialized Form

Field Summary
Fields inherited from class hep.aida.bin.DynamicBin1D
elements, fixedOrder, isIncrementalStatValid, isSorted, isSumOfInversionsValid, isSumOfLogarithmsValid, sortedElements
Fields inherited from class hep.aida.bin.QuantileBin1D
Fields inherited from class hep.aida.bin.MightyStaticBin1D
hasSumOfInversions, hasSumOfLogarithms, sumOfInversions, sumOfLogarithms, sumOfPowers
Fields inherited from class hep.aida.bin.StaticBin1D
arguments, max, min, size, sum, sum_xx
Fields inherited from class cern.colt.PersistentObject
Constructor Summary
EmpiricalDistribution(RandomVariable X, int t, int N)
          Constructs a hep.aida.bin.DynamicBin1D, fills it with N samples of X conditioned on information available at time t.
Method Summary
 int get_nSamples()
          The number of samples.
 void set_nSamples(int N)
          Update the sample size.
Methods inherited from class hep.aida.bin.DynamicBin1D
add, addAllOfFromTo, aggregate, clear, clearAllMeasures, clone, correlation, covariance, elements_unsafe, elements, equals, frequencies, getMaxOrderForSumOfPowers, getMinOrderForSumOfPowers, invalidateAll, isRebinnable, max, min, moment, quantile, quantileInverse, quantiles, removeAllOf, sample, sampleBootstrap, setFixedOrder, size, sort, sortedElements_unsafe, sortedElements, standardize, sum, sumOfInversions, sumOfLogarithms, sumOfPowers, sumOfSquares, toString, trim, trimmedMean, trimToSize, updateIncrementalStats, updateSumOfInversions, updateSumOfLogarithms, validateAll
Methods inherited from class hep.aida.bin.QuantileBin1D
compareWith, median, sizeOfRange, splitApproximately, splitApproximately
Methods inherited from class hep.aida.bin.MightyStaticBin1D
geometricMean, harmonicMean, hasSumOfInversions, hasSumOfLogarithms, hasSumOfPowers, kurtosis, product, setMaxOrderForSumOfPowers, skew, xcheckOrder, xequals, xhasSumOfPowers, xisLegalOrder
Methods inherited from class hep.aida.bin.AbstractBin1D
addAllOf, buffered, mean, relError, rms, standardDeviation, standardError, variance
Methods inherited from class hep.aida.bin.AbstractBin
center, center, error, error, offset, offset, value, value
Methods inherited from class java.lang.Object
finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail


public EmpiricalDistribution(RandomVariable X,
                             int t,
                             int N)
Constructs a hep.aida.bin.DynamicBin1D, fills it with N samples of X conditioned on information available at time t.

X - random variable supplying the distribution.
t - current time (determines information to condition on).
N - number of (conditional) samples.
Method Detail


public int get_nSamples()
The number of samples.


public void set_nSamples(int N)
Update the sample size.