public Object parseXMLObject(XMLObject xo) throws XMLParseException {

          XMLObject cxo = xo.getChild(MVN_MEAN);
          Parameter mean = (Parameter) cxo.getChild(Parameter.class);

          cxo = xo.getChild(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");

          Transform[] transforms = parseListOfTransforms(xo, mean.getDimension());

          MultivariateDistributionLikelihood likelihood =
              new MultivariateDistributionLikelihood(
                  new MultivariateNormalDistribution(
                      mean.getParameterValues(), precision.getParameterAsMatrix()),
                  transforms);
          cxo = xo.getChild(DATA);
          if (cxo != null) {
            for (int j = 0; j < cxo.getChildCount(); j++) {
              if (cxo.getChild(j) instanceof Parameter) {
                Parameter data = (Parameter) cxo.getChild(j);
                if (data instanceof MatrixParameter) {
                  MatrixParameter matrix = (MatrixParameter) data;
                  if (matrix.getParameter(0).getDimension() != mean.getDimension())
                    throw new XMLParseException(
                        "dim("
                            + data.getStatisticName()
                            + ") = "
                            + matrix.getParameter(0).getDimension()
                            + " is not equal to dim("
                            + mean.getStatisticName()
                            + ") = "
                            + mean.getDimension()
                            + " in "
                            + xo.getName()
                            + "element");

                  for (int i = 0; i < matrix.getParameterCount(); i++) {
                    likelihood.addData(matrix.getParameter(i));
                  }
                } else {
                  if (data.getDimension() != mean.getDimension())
                    throw new XMLParseException(
                        "dim("
                            + data.getStatisticName()
                            + ") = "
                            + data.getDimension()
                            + " is not equal to dim("
                            + mean.getStatisticName()
                            + ") = "
                            + mean.getDimension()
                            + " in "
                            + xo.getName()
                            + "element");
                  likelihood.addData(data);
                }
              } else {
                throw new XMLParseException("illegal element in " + xo.getName() + " element");
              }
            }
          }

          return likelihood;
        }
        public Object parseXMLObject(XMLObject xo) throws XMLParseException {

          XMLObject cxo = xo.getChild(DistributionLikelihoodParser.DISTRIBUTION);
          ParametricMultivariateDistributionModel distribution =
              (ParametricMultivariateDistributionModel)
                  cxo.getChild(ParametricMultivariateDistributionModel.class);

          // Parse transforms here
          int maxDim = distribution.getMean().length;
          Transform[] transforms = parseListOfTransforms(xo, maxDim);

          MultivariateDistributionLikelihood likelihood =
              new MultivariateDistributionLikelihood(xo.getId(), distribution, transforms);

          boolean dataAsMatrix = xo.getAttribute(DATA_AS_MATRIX, false);

          cxo = xo.getChild(DATA);
          if (cxo != null) {
            for (int j = 0; j < cxo.getChildCount(); j++) {
              if (cxo.getChild(j) instanceof Parameter) {
                Parameter data = (Parameter) cxo.getChild(j);
                if (data instanceof MatrixParameter) {
                  MatrixParameter matrix = (MatrixParameter) data;
                  if (dataAsMatrix) {
                    likelihood.addData(matrix);
                  } else {
                    if (matrix.getParameter(0).getDimension() != distribution.getMean().length)
                      throw new XMLParseException(
                          "dim("
                              + data.getStatisticName()
                              + ") = "
                              + matrix.getParameter(0).getDimension()
                              + " is not equal to dim("
                              + distribution.getType()
                              + ") = "
                              + distribution.getMean().length
                              + " in "
                              + xo.getName()
                              + "element");

                    for (int i = 0; i < matrix.getParameterCount(); i++) {
                      likelihood.addData(matrix.getParameter(i));
                    }
                  }
                } else {
                  if (data.getDimension() != distribution.getMean().length)
                    throw new XMLParseException(
                        "dim("
                            + data.getStatisticName()
                            + ") = "
                            + data.getDimension()
                            + " is not equal to dim("
                            + distribution.getType()
                            + ") = "
                            + distribution.getMean().length
                            + " in "
                            + xo.getName()
                            + "element");
                  likelihood.addData(data);
                }
              } else {
                throw new XMLParseException("illegal element in " + xo.getName() + " element");
              }
            }
          }

          return likelihood;
        }