T - class type of the observations' and states' discrete variablespublic class MultiObsDistributionIndepGaussians<T extends Copyable<?>> extends AbstractMultiObservationDistributionIndep<T,T> implements SamplingDistribution<AbstractMultiState<T>>, FirstOrderMoment<AbstractMultiState<T>>, SecondOrderCentralMoment<Jama.Matrix[]>
| Modifier and Type | Field and Description |
|---|---|
protected GaussianDistribution[] |
gaussian
multivariate gaussian density object for evaluation
|
protected Jama.Matrix[] |
H
state-to-observation-space linear transform matrix
|
protected Jama.Matrix[] |
R
Gaussian measurement noise covariance matrix
|
condX, factoryX, factoryZ| Modifier and Type | Method and Description |
|---|---|
AbstractMultiState<T> |
drawSample()
Generate a new sample from this density.
|
Jama.Matrix[] |
getCovariance() |
AbstractMultiState<T> |
getMean() |
int |
getNumOfIndeps() |
Jama.Matrix[] |
getObservationMatrices() |
double |
log_p(AbstractMultiState<T> Z)
Evaluate natural logarithm of p(X) at location x. log(P(X=x))
|
double |
log_p(AbstractMultiState<T> Z,
int i)
Evaluate the density independently for observation i in Z conditional on state i in X
|
double |
log_p(AbstractMultiState<T> Z,
int i,
int j)
Evaluate the density independently for observation i in Z conditional on state j in X
|
double |
logp(AbstractMultiState<T> Z) |
double |
p(AbstractMultiState<T> Z)
Evaluate p(X) at location x.
|
double |
p(AbstractMultiState<T> Z,
int i)
Evaluate the density independently for observation i in Z conditional on state i in X
|
double |
p(AbstractMultiState<T> Z,
int i,
int j)
Evaluate the density independently for observation i in Z conditional on state j in X
|
void |
setCondition(AbstractMultiState<T> X)
Set the conditional variable
|
getConditionprotected Jama.Matrix[] H
protected Jama.Matrix[] R
protected GaussianDistribution[] gaussian
public MultiObsDistributionIndepGaussians(Random rand, Jama.Matrix H, Jama.Matrix R, AbstractMultiState<T> X, AbstractMultiStateFactory<T> factoryX, AbstractMultiStateFactory<T> factoryZ) throws IllegalArgumentException
H - state-to-observation-space linear transform matrixR - Gaussian noise covariance matrixX - condition statefactoryX - factory to determine multi-target state layoutfactoryZ - factory to determine multi-target observation layoutIllegalArgumentException - if any dimensions of the input objects do not matchpublic MultiObsDistributionIndepGaussians(Random rand, Jama.Matrix[] H, Jama.Matrix[] R, AbstractMultiState<T> X, AbstractMultiStateFactory<T> factoryX, AbstractMultiStateFactory<T> factoryZ) throws IllegalArgumentException
H - state-to-observation-space linear transform matricesR - Gaussian noise covariance matricesX - condition statefactoryX - factory to determine multi-target state layoutfactoryZ - factory to determine multi-target observation layoutIllegalArgumentException - if any dimensions of the input objects do not matchpublic MultiObsDistributionIndepGaussians(Random rand, Jama.Matrix H, Jama.Matrix R, MultiStateDistributionIndepGaussians<T> distribX, AbstractMultiStateFactory<T> factoryX, AbstractMultiStateFactory<T> factoryZ) throws IllegalArgumentException
H - state-to-observation-space linear transform matrixR - Gaussian noise covariance matrixdistribX - A Gaussian state distributionfactoryX - factory to determine multi-target state layoutfactoryZ - factory to determine multi-target observation layoutIllegalArgumentException - if any dimensions of the input objects do not matchpublic MultiObsDistributionIndepGaussians(Random rand, Jama.Matrix[] H, Jama.Matrix[] R, MultiStateDistributionIndepGaussians<T> distribX, AbstractMultiStateFactory<T> factoryX, AbstractMultiStateFactory<T> factoryZ) throws IllegalArgumentException
H - state-to-observation-space linear transform matricesR - Gaussian noise covariance matricesX - condition statefactoryX - factory to determine multi-target state layoutfactoryZ - factory to determine multi-target observation layoutIllegalArgumentException - if any dimensions of the input objects do not matchpublic MultiObsDistributionIndepGaussians(Random rand, Jama.Matrix H, Jama.Matrix R, MultiStateLinTransDistributionIndepGaussians<T> transdistribX, AbstractMultiStateFactory<T> factoryX, AbstractMultiStateFactory<T> factoryZ) throws IllegalArgumentException
H - state-to-observation-space linear transform matrixR - Gaussian noise covariance matrixtransdistribX - A Gaussian state distributionfactoryX - factory to determine multi-target state layoutfactoryZ - factory to determine multi-target observation layoutIllegalArgumentException - if any dimensions of the input objects do not matchpublic MultiObsDistributionIndepGaussians(Random rand, Jama.Matrix[] H, Jama.Matrix[] R, MultiStateLinTransDistributionIndepGaussians<T> transdistribX, AbstractMultiStateFactory<T> factoryX, AbstractMultiStateFactory<T> factoryZ) throws IllegalArgumentException
H - state-to-observation-space linear transform matricesR - Gaussian noise covariance matricesX - condition statefactoryX - factory to determine multi-target state layoutfactoryZ - factory to determine multi-target observation layoutIllegalArgumentException - if any dimensions of the input objects do not matchpublic double logp(AbstractMultiState<T> Z) throws IllegalArgumentException
IllegalArgumentExceptionpublic double p(AbstractMultiState<T> Z) throws IllegalArgumentException
EvaluatableDistributionp in interface EvaluatableDistribution<AbstractMultiState<T extends Copyable<?>>>p in class AbstractMultiObservationDistributionIndep<T extends Copyable<?>,T extends Copyable<?>>Z - realization of random variable XIllegalArgumentExceptionpublic double p(AbstractMultiState<T> Z, int i)
AbstractMultiObservationDistributionIndepp in interface IndependentlyEvaluatableDistribution<AbstractMultiState<T extends Copyable<?>>>p in class AbstractMultiObservationDistributionIndep<T extends Copyable<?>,T extends Copyable<?>>Z - realization of random variable Xi - i-th element in xpublic double p(AbstractMultiState<T> Z, int i, int j)
AbstractMultiObservationDistributionIndeppublic double log_p(AbstractMultiState<T> Z)
LogEvaluatableDistributionlog_p in interface LogEvaluatableDistribution<AbstractMultiState<T extends Copyable<?>>>log_p in class AbstractMultiObservationDistributionIndep<T extends Copyable<?>,T extends Copyable<?>>Z - realization of random variable Xpublic double log_p(AbstractMultiState<T> Z, int i)
AbstractMultiObservationDistributionIndeplog_p in interface LogIndependentlyEvaluatableDistribution<AbstractMultiState<T extends Copyable<?>>>log_p in class AbstractMultiObservationDistributionIndep<T extends Copyable<?>,T extends Copyable<?>>Z - realization of random variable Xi - i-th element in xpublic double log_p(AbstractMultiState<T> Z, int i, int j)
AbstractMultiObservationDistributionIndeppublic AbstractMultiState<T> getMean()
getMean in interface FirstOrderMoment<AbstractMultiState<T extends Copyable<?>>>public AbstractMultiState<T> drawSample()
SamplingDistributiondrawSample in interface SamplingDistribution<AbstractMultiState<T extends Copyable<?>>>public Jama.Matrix[] getCovariance()
getCovariance in interface SecondOrderCentralMoment<Jama.Matrix[]>public void setCondition(AbstractMultiState<T> X)
ConditionalDistributionsetCondition in interface ConditionalDistribution<AbstractMultiState<T extends Copyable<?>>>setCondition in class AbstractMultiObservationDistribution<T extends Copyable<?>,T extends Copyable<?>>X - conditional variablepublic Jama.Matrix[] getObservationMatrices()
public int getNumOfIndeps()
getNumOfIndeps in class AbstractMultiObservationDistributionIndep<T extends Copyable<?>,T extends Copyable<?>>Copyright © 2010–2020 Martin Luther University Halle-Wittenberg, Institute of Computer Science, Pattern Recognition and Bioinformatics. All rights reserved.