Examples of com.statslibextensions.statistics.distribution.ScaledInverseGammaCovDistribution

Package com.statslibextensions.statistics.distribution

Examples of com.statslibextensions.statistics.distribution.ScaledInverseGammaCovDistribution

com.statslibextensions.statistics.distribution.ScaledInverseGammaCovDistribution

       */
      mvGaussianBayesUpdate(augResponseDist, priorGlobalMeanSample,
          updatedBetaMean, observation.getObservedData());


      final Vector betaMeanError = postBetaSmoothedSample.minus(priorBetaSmoothedSample);
      final ScaledInverseGammaCovDistribution updatedBetaCov = particle.getPriorBetaCov().clone();
      updateCovariancePrior(updatedBetaCov, betaMeanError);
      final Matrix betaCovSmpl = updatedBetaCov.sample(random);
      Preconditions.checkState(betaCovSmpl.getElement(0, 0) >= 0d);
      updatedBetaMean.setCovariance(lambdaSamplesMatrix.times(betaCovSmpl
          .times(updatedBetaMean.getCovariance())));


      /*
       * Now, do the above for the the global mean term.
       */
      final MultivariateGaussian updatedGlobalMean =
          particle.getPriorBeta().times(particle.getAugmentedResponseDistribution());


      mvGaussianBayesUpdate(augResponseDist,
          observation.getObservedData().times(priorBetaSmoothedSample), updatedGlobalMean,
          MatrixFactory.getDenseDefault().createIdentity(
            augResponseDist.getInputDimensionality(), augResponseDist.getInputDimensionality()));


      final Vector globalMeanError = postGlobalMeanSample.minus(priorGlobalMeanSample);
      final ScaledInverseGammaCovDistribution updatedGlobalMeanCov =
          particle.getPriorBetaCov().clone();
      updateCovariancePrior(updatedGlobalMeanCov, globalMeanError);
      final Matrix globalMeanCovSmpl = updatedGlobalMeanCov.sample(random)
          .times(updatedGlobalMean.getCovariance());
      Preconditions.checkState(globalMeanCovSmpl.getElement(0, 0) > 0d);
      updatedGlobalMean.setCovariance(globalMeanCovSmpl);


      final PolyaGammaLogitDistribution updatedParticle =

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     */


    final Vector betaCovPriorMean = VectorFactory.getDefault().copyArray(
      new double[] {200d});
    final double betaPriorCovDof = 2 + betaCovPriorMean.getDimensionality();
    final ScaledInverseGammaCovDistribution priorBetaCov =
        new ScaledInverseGammaCovDistribution(betaCovPriorMean.getDimensionality(),  
          betaCovPriorMean.scale(betaPriorCovDof - 1d).getElement(0), 
          betaPriorCovDof);
    final MultivariateGaussian priorBeta =
        new MultivariateGaussian(VectorFactory.getDenseDefault().copyArray(
            new double[] {0d}), priorBetaCov.getMean());


    /*
     * Create and initialize the PL filter
     */
    final PolyaGammaLogitPLFilter plFilter =

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        new DefaultDataDistribution<LassoRegressionDistribution>();
    for (final LassoRegressionDistribution particle : resampledParticles) {


      final MultivariateGaussian postBeta = particle.getPriorBeta().clone();


      final ScaledInverseGammaCovDistribution postObsCov = particle.getPriorObsCov();


      final Matrix augCovLassoSample = this.augLassoDist.sample(random);
      final Matrix obsCovSample = postObsCov.sample(random);
      
      /*
       * Update sufficient stats.
       * TODO FIXME not done
       */

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Related Classes of com.statslibextensions.statistics.distribution.ScaledInverseGammaCovDistribution

plm.lasso.LassoRegressionPLFilter

plm.logit.polyagamma.PolyaGammaLogitPLFilter

plm.util.logit.polyagamma.PolyaGammaLogitRunner

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