Package gov.sandia.cognition.statistics.distribution

Examples of gov.sandia.cognition.statistics.distribution.MultivariateGaussian.sample()

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         */
        final double scaleSample = thisPriorScale.sample(this.rng);

        final MultivariateGaussian thisPriorOffset = initialPriorPsi.clone();

        final Vector systemSample = thisPriorOffset.sample(this.rng);
        final Vector offsetTerm = systemSample.subVector(0,
            systemSample.getDimensionality()/2 - 1);
        thisKf.getModel().setState(offsetTerm);
        thisKf.setCurrentInput(offsetTerm);

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            thisKf.getModel().getStateDimensionality(),
            thisKf.getModel().getStateDimensionality());
        thisKf.setModelCovariance(modelIdent.scale(scaleSample));

        final MultivariateGaussian priorState = thisKf.createInitialLearnedObject();
        final Vector priorStateSample = priorState.sample(this.rng);

        final GaussianArHpWfParticle particle =
            new GaussianArHpWfParticle(thisKf,
                ObservedValue.<Vector>create(0, null), priorState,
                priorStateSample,
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      final Matrix H = MatrixFactory.getDefault().createMatrix(xDim, xDim * 2);
      H.setSubMatrix(0, 0, Ij);
      H.setSubMatrix(0, xDim, MatrixFactory.getDefault().createDiagonal(predState.getStateSample()));
      final Vector postStateSample = posteriorState.sample(this.rng);
      final MultivariateGaussian priorPhi = predState.getPsiSS();
      final Vector phiPriorSmpl = priorPhi.sample(this.rng);
      final Vector xHdiff = postStateSample.minus(H.times(phiPriorSmpl));

      final double newN = scaleSS.getShape() + 1d;
      final double d = scaleSS.getScale() + xHdiff.dotProduct(xHdiff);
     
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          H.transpose().times(postStateSample)));
      final MultivariateGaussian postPhi = systemOffsetsSS;
      postPhi.setMean(postPhiMean);
      postPhi.setCovariance(postAInv.scale(newScaleSmpl));
     
      final Vector postPhiSmpl = postPhi.sample(this.rng);
      final Matrix smplArTerms = MatrixFactory.getDefault().createDiagonal(
          postPhiSmpl.subVector(
              postPhiSmpl.getDimensionality()/2,
              postPhiSmpl.getDimensionality() - 1));
      kf.getModel().setA(smplArTerms);
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      predictivePrior.setMean(G.times(predictivePrior.getMean()));
      predictivePrior.setCovariance(
          G.times(predictivePrior.getCovariance()).times(G.transpose())
            .plus(kf.getModelCovariance()));
      final Vector betaSample =
          predictivePrior.sample(getRandom());
//          predictivePrior.getMean();
      final double predPriorObsMean =
            F.times(betaSample).getElement(0);

      final int particleCount;
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