riso.belief_nets.BeliefNetwork rcs
{
    % theta is a continuous root variable, with a uniform
    % distribution as its prior.
    riso.belief_nets.Variable theta
    {
        type continuous
        distribution riso.distributions.Uniform { a 0  b 6.28318 }
    }

    % T is a discrete root variable, with a uniform prior.
    riso.belief_nets.Variable T
    {
        type discrete
        distribution riso.distributions.Discrete
        {
            dimensions { 3 }    % number of elements in list on next line
            probabilities { 0.3333 0.3333 0.3334 }
        }
    }

    % RCS is a continuous child variable.
    riso.belief_nets.Variable RCS
    {
        type continuous
        parents { theta T }

        % The conditional distribution of RCS has one continuous and one
        % discrete parent -- the discrete one act as an index for 
        % conditional distributions which take the continuous variable
        % as a parent.
        distribution riso.distributions.IndexedDistribution
        {
            % Next line shows T is an index or selector for the 
            % three functions which characterize RCS.
            index-variables { T }

            % Here is the list of the three functions indexed by T.
            % Each component takes theta as a parent.
            components
            {
                riso.distributions.RegressionDensity
                {
                    % ``RadarCrossSection'' is a non-standard distribution
                    % type devised for this problem. It is described by
                    % the parameters A, B, and C. The RadarCrossSection
                    % code knows how to parse this description.
                    regression-model riso.regression.RadarCrossSection
                        { A 30  B 2  C 0 }
                    noise-model riso.distributions.Gaussian { mean 0 std-deviation 1 }
                }
                riso.distributions.RegressionDensity
                {
                    regression-model riso.regression.RadarCrossSection
                        { A 30  B 10  C 20 }
                    noise-model riso.distributions.Gaussian { mean 0 std-deviation 1 }
                }
                riso.distributions.RegressionDensity
                {
                    regression-model riso.regression.RadarCrossSection
                        { A 20  B 1.5  C 10 }
                    noise-model riso.distributions.Gaussian { mean 0 std-deviation 1 }
                }
            }
        }
    }
}