Examples of MultivariateGaussian

gov.sandia.cognition.statistics.distribution.MultivariateGaussian

@author Vincent Garcia @author Frank Nielsen @version 1.0 @section LicenseSee file LICENSE.txt @section DescriptionThe multivariate Gaussian distribution is an exponential family and, as a consequence, the probability density function is given by \f[ f(x; \mathbf{\Theta}) = \exp \left( \langle t(x), \mathbf{\Theta} \rangle - F(\mathbf{\Theta}) + k(x) \right) \f] where \f$ \mathbf{\Theta} \f$ are the natural parameters. This class implements the different functions allowing to express a multivariate Gaussian distribution as a member of an exponential family. @section ParametersThe parameters of a given distribution are: - Source parameters \f$\mathbf{\Lambda} = ( \mu , \Sigma ) \mbox{ with } \mu \in \mathds{R}^d \mbox{ and } \Sigma \succ 0\f$ - Natural parameters \f$\mathbf{\Theta} = ( \theta , \Theta )\f$ - Expectation parameters \f$\mathbf{H} = ( \eta , H )\f$

      Matrix priorPredObsCov = X.times(
          particle.getPriorBeta().getCovariance())
          .times(X.transpose()).plus(particle.augLassoSample);
      priorPredObsCov.times(particle.priorObsCovSample);


      final MultivariateGaussian priorPredictiveObsDist=
          new MultivariateGaussian(priorPredObsMean, priorPredObsCov);


      return priorPredictiveObsDist.getProbabilityFunction().logEvaluate(y);
    }

  public static MixtureModel loadMixtureModel(String path, BufferedImage image, int n){
    MixtureModel mm = MixtureModel.load(path);
    if (mm==null){
      PVector[]         px       = convertColorImageToPointSet3D(image);
      Vector<PVector>[] clusters = KMeans.run(px, n);
      mm = BregmanSoftClustering.initialize(clusters, new MultivariateGaussian());
      mm = BregmanSoftClustering.run(px, mm);
      MixtureModel.save(mm, path);
    }
    return mm;
  }

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Examples of MultivariateGaussian

Examples of gov.sandia.cognition.statistics.distribution.MultivariateGaussian

Examples of jMEF.MultivariateGaussian