Examples with MultivariateNormalDistributionModel dr.inference.distribution.MultivariateNormalDistributionModel used on opensource projects

Example 1 with MultivariateNormalDistributionModel

use of dr.inference.distribution.MultivariateNormalDistributionModel in project beast-mcmc by beast-dev.

the class MultivariateNormalDistributionModelParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    XMLObject cxo = xo.getChild(MultivariateDistributionLikelihood.MVN_MEAN);
    Parameter mean = (Parameter) cxo.getChild(Parameter.class);
    cxo = xo.getChild(MultivariateDistributionLikelihood.MVN_PRECISION);
    MatrixParameter precision = (MatrixParameter) cxo.getChild(MatrixParameter.class);
    if (mean.getDimension() != precision.getRowDimension() || mean.getDimension() != precision.getColumnDimension())
        throw new XMLParseException("Mean and precision have wrong dimensions in " + xo.getName() + " element");
    return new MultivariateNormalDistributionModel(mean, precision);
}

Example 2 with MultivariateNormalDistributionModel

use of dr.inference.distribution.MultivariateNormalDistributionModel in project beast-mcmc by beast-dev.

the class EllipticalSliceOperatorParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    final double weight = xo.getDoubleAttribute(MCMCOperator.WEIGHT);
    final Parameter variable = (Parameter) xo.getChild(Parameter.class);
    boolean drawByRowTemp = false;
    if (xo.hasAttribute(DRAW_BY_ROW)) {
        drawByRowTemp = xo.getBooleanAttribute(DRAW_BY_ROW);
    }
    final boolean drawByRow = drawByRowTemp;
    boolean signal = xo.getAttribute(SIGNAL_CONSTITUENT_PARAMETERS, true);
    if (!signal) {
        Parameter possiblyCompound = variable;
        if (variable instanceof MaskedParameter) {
            possiblyCompound = ((MaskedParameter) variable).getUnmaskedParameter();
        }
        if (!(possiblyCompound instanceof CompoundParameter)) {
            signal = true;
        }
    }
    double bracketAngle = xo.getAttribute(BRACKET_ANGLE, 0.0);
    boolean translationInvariant = xo.getAttribute(TRANSLATION_INVARIANT, false);
    boolean rotationInvariant = xo.getAttribute(ROTATION_INVARIANT, false);
    GaussianProcessRandomGenerator gaussianProcess = (GaussianProcessRandomGenerator) xo.getChild(GaussianProcessRandomGenerator.class);
    if (gaussianProcess == null) {
        final MultivariateDistributionLikelihood likelihood = (MultivariateDistributionLikelihood) xo.getChild(MultivariateDistributionLikelihood.class);
        if (!(likelihood.getDistribution() instanceof GaussianProcessRandomGenerator)) {
            throw new XMLParseException("Elliptical slice sampling only works for multivariate normally distributed random variables");
        }
        if (likelihood.getDistribution() instanceof MultivariateNormalDistribution)
            gaussianProcess = (MultivariateNormalDistribution) likelihood.getDistribution();
        if (likelihood.getDistribution() instanceof MultivariateNormalDistributionModel)
            gaussianProcess = (MultivariateNormalDistributionModel) likelihood.getDistribution();
    }
    EllipticalSliceOperator operator = new EllipticalSliceOperator(variable, gaussianProcess, drawByRow, signal, bracketAngle, translationInvariant, rotationInvariant);
    operator.setWeight(weight);
    return operator;
}