gov.sandia.cognition.statistics.bayesian

Class BayesianLinearRegression.IncrementalEstimator.SufficientStatistic

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.AbstractSufficientStatistic<InputOutputPair<? extends Vectorizable,java.lang.Double>,MultivariateGaussian>

gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression.IncrementalEstimator.SufficientStatistic

All Implemented Interfaces:

Factory<MultivariateGaussian>, SufficientStatistic<InputOutputPair<? extends Vectorizable,java.lang.Double>,MultivariateGaussian>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

BayesianLinearRegression.IncrementalEstimator

public class BayesianLinearRegression.IncrementalEstimator.SufficientStatistic
extends AbstractSufficientStatistic<InputOutputPair<? extends Vectorizable,java.lang.Double>,MultivariateGaussian>

SufficientStatistic for incremental Bayesian linear regression

See Also:

Serialized Form

Field Summary

Fields inherited from class gov.sandia.cognition.statistics.AbstractSufficientStatistic

count

Constructor Summary

Constructors

Constructor and Description
SufficientStatistic(MultivariateGaussian prior) Creates a new instance of SufficientStatistic

Method Summary

Modifier and Type	Method and Description
MultivariateGaussian.PDF	create() Creates a new instance of an object.
void	create(MultivariateGaussian distribution) Modifies the given distribution with the parameters indicated by the sufficient statistics
Matrix	getCovarianceInverse() Getter for covarianceInverse
int	getDimensionality() Gets the dimensionality of the underlying Gaussian
Vector	getMean() Computes the mean of the Gaussian, but involves a matrix inversion and multiplication, so it's expensive.
Vector	getZ() Getter for z
void	update(InputOutputPair<? extends Vectorizable,java.lang.Double> value) Updates the sufficient statistics from the given value

Methods inherited from class gov.sandia.cognition.statistics.AbstractSufficientStatistic

clone, getCount, setCount, update

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

SufficientStatistic

public SufficientStatistic(MultivariateGaussian prior)

Creates a new instance of SufficientStatistic

Parameters:

prior - Prior on the weights

Method Detail

update

public void update(InputOutputPair<? extends Vectorizable,java.lang.Double> value)

Description copied from interface: SufficientStatistic

Updates the sufficient statistics from the given value

Parameters:

value - Value to update the sufficient statistics

create

public MultivariateGaussian.PDF create()

Description copied from interface: Factory

Creates a new instance of an object.

Returns:

A newly created object.

create

public void create(MultivariateGaussian distribution)

Description copied from interface: SufficientStatistic

Modifies the given distribution with the parameters indicated by the sufficient statistics

Parameters:

distribution - Distribution to modify by side effect

getCovarianceInverse

public Matrix getCovarianceInverse()

Getter for covarianceInverse

Returns:

Covariance inverse, sometimes called "precision"

getZ

public Vector getZ()

Getter for z

Returns:

"z" statistic, proportional to the mean

getMean

public Vector getMean()

Computes the mean of the Gaussian, but involves a matrix inversion and multiplication, so it's expensive.

Returns:

Mean of the Gaussian.

getDimensionality

public int getDimensionality()

Gets the dimensionality of the underlying Gaussian

Returns:

Dimensionality of the underlying Gaussian