Package gov.sandia.cognition.statistics.distribution

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

104
105
106
107
108
109
110
111
112
113
114
115
116
117
      // TODO FIXME
      final Matrix stPriorPredPrec = mPriorPredCov.inverse().scale(
          transParticle.getSigma2SS().getShape()/transParticle.getSigma2SS().getScale());


      MultivariateStudentTDistribution mPriorPredDist = new MultivariateStudentTDistribution(
          transParticle.getSigma2SS().getShape(),
          mPriorPredMean, stPriorPredPrec);

      final double logCt = mPriorPredDist.getProbabilityFunction().logEvaluate(
          observation.getObservedValue());

      return logCt;
    }
View Full Code Here
198
199
200
201
202
203
204
205
206
207
208
209
210
211
      final InverseGammaDistribution sigma2SS = predState.getSigma2SS().clone();
      // TODO FIXME matrix inverse!!
      final Matrix postStatePrec = posteriorState.getCovarianceInverse().scale(
          sigma2SS.getShape()/sigma2SS.getScale());
      MultivariateStudentTDistribution postStateMarginal = new MultivariateStudentTDistribution(
          sigma2SS.getShape(),
          posteriorState.getMean(), postStatePrec);
      final Vector postStateSample = postStateMarginal.sample(this.rng);
     
      final Vector psiPriorSmpl = predState.getPsiSample();
      // x_t
      final Vector xHdiff = postStateSample.minus(H.times(psiPriorSmpl));
View Full Code Here
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
              - centeringDist.getInputDimensionality() + 1d;
      final double kappa = centeringDist.getCovarianceDivisor();
      final Matrix newComponentPriorPredPrecision =
          centeringDist.getInverseWishart().getInverseScale()
              .scale(2d * (kappa + 1d) / (kappa * newComponentPriorPredDof));
      final MultivariateStudentTDistribution newComponentPriorPred =
          new MultivariateStudentTDistribution(newComponentPriorPredDof, centeringDist
              .getGaussian().getMean(), newComponentPriorPredPrecision.inverse());

      final double newComponentLogLikelihood =
          Math.log(particle.getAlpha()) - Math.log(particle.getAlpha() + particle.getIndex())
              + newComponentPriorPred.getProbabilityFunction().logEvaluate(observation);
      componentPriorPredLogLikelihoods[0] = newComponentLogLikelihood;

      double totalLogLikelihood = newComponentLogLikelihood;

      /*
       * Now, evaluate log likelihood for the current mixture components
       */
      int n = 0;
      for (final MultivariateGaussian component : particle.getDistributions()) {

        final double componentN = particle.getCounts().getElement(n);
        final double componentPriorPredDof =
            2d * centeringDist.getInverseWishart().getDegreesOfFreedom() + componentN
                - centeringDist.getInputDimensionality() + 1d;
        final Vector componentPriorPredMean =
            centeringDist.getGaussian().getMean().scale(kappa)
                .plus(component.getMean().scale(componentN)).scale(1d / (kappa + componentN));


        final Vector componentCenteringMeanDiff =
            centeringDist.getGaussian().getMean().minus(component.getMean());
        final Matrix componentD =
            component.getCovariance().plus(
                componentCenteringMeanDiff.outerProduct(componentCenteringMeanDiff).scale(
                    kappa * componentN / (kappa + componentN)));

        final Matrix componentPriorPredCovariance =
            centeringDist
                .getInverseWishart()
                .getInverseScale()
                .plus(componentD.scale(1d / 2d))
                .scale(
                    2d * (kappa + componentN + 1d) / ((kappa + componentN) * componentPriorPredDof));

        // FIXME TODO avoid this inverse!
        final MultivariateStudentTDistribution componentPriorPred =
            new MultivariateStudentTDistribution(componentPriorPredDof, componentPriorPredMean,
                componentPriorPredCovariance.inverse());

        final double componentLogLikelihood =
            Math.log(componentN) - Math.log(particle.getAlpha() + particle.getIndex())
                + componentPriorPred.getProbabilityFunction().logEvaluate(observation);

        componentPriorPredLogLikelihoods[n + 1] = componentLogLikelihood;
        totalLogLikelihood = LogMath.add(totalLogLikelihood, componentLogLikelihood);
        n++;
      }
View Full Code Here
350
351
352
353
354
355
356
357
358
359
360
361
362
363
         */
        final InverseGammaDistribution sigma2SS = particle.getSigma2SS();
        // TODO FIXME matrix inverse!!
        final Matrix postStatePrec = particle.getState().getCovarianceInverse().scale(
            sigma2SS.getShape()/sigma2SS.getScale());
        MultivariateStudentTDistribution postStateMarginal = new MultivariateStudentTDistribution(
            sigma2SS.getShape(),
            particle.getState().getMean(), postStatePrec);
        final Vector stateSample = postStateMarginal.sample(this.getRandom());

        final GaussianArHpWfParticle transStateTmp =
            new GaussianArHpWfParticle(particle.getPrevParticle(),
                particle.getFilter(),
                particle.getObs(),
View Full Code Here
TOP

Related Classes of gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution

  • plm.clustering.MvGaussianDPPLFilter$MultivariateGaussianDPPLUpdater
  • plm.gaussian.GaussianArHpWfPlFilter
  • plm.gaussian.GaussianArHpWfPlFilter$GaussianArHpWfPlUpdater
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.