function CPD = root_gaussian_CPD(bnet, self, mu, Sigma, mu0, n0, alpha0, beta0)% ROOT_GAUSSIAN_CPD Make an unconditional Gaussian distrib.%% CPD = root_gaussian_CPD(bnet, self, mu, Sigma)% This defines the distribution Y ~ N(mu, Sigma),% Pass in [] to generate a default random value for a parameter.%% CPD = root_gaussian_CPD(bnet, self, [], [], mu0, n0, alpha0, beta0)% defines a Normal-Wishart prior over the parameters:%   P(mu | lambda) = N(mu | mu0, n0*lambda)%   P(lambda) = Wishart(lambda | alpha0, beta0)% where lambda = inv(Sigma) is the precision matrix of mu.% n0 is a scale factor, beta0 is a precision matrix.% Pass in [] to generate a default value for a hyperparameter.% mu and Sigma will be set to their prior expected values.% See "Bayesian Theory", Bernardo and Smith (2000), p441.if nargin==0  % This occurs if we are trying to load an object from a file.  CPD = init_fields;  CPD = class(CPD, 'root_gaussian_CPD', generic_CPD(0));  return;elseif isa(bnet, 'root_gaussian_CPD')  % This might occur if we are copying an object.  CPD = bnet;  return;endCPD = init_fields;ns = bnet.node_sizes;d = ns(self);if nargin < 5,  prior = [];  if isempty(mu), mu = randn(d, 1); end  if isempty(Sigma), Sigma = eye(d); endelse  if isempty(mu0), mu0 = zeros(d, 1); end  if isempty(n0), n0 = 0.1; end  if isempty(alpha0), alpha0 = (d-1)/2 + 1; end % Wishart requires 2 alpha > d-1  if isempty(beta0), beta0 = eye(d); end    prior.mu = mu0;  prior.n = n0;  prior.alpha = alpha0;  prior.beta = beta0;    % set params to their mean  mu = prior.mu;  Sigma = prior.beta/prior.alpha; % mean of Wishart is E[lambda] = alpha*inv(beta)endCPD.self = self;CPD.mu = mu;CPD.Sigma = Sigma;CPD.prior = prior;clamped = 0;CPD = class(CPD, 'root_gaussian_CPD', generic_CPD(clamped));%%%%%%%%%%%function CPD = init_fields()% This ensures we define the fields in the same order % no matter whether we load an object from a file,% or create it from scratch. (Matlab requires this.)CPD.self = [];CPD.mu = [];CPD.Sigma = [];CPD.prior = [];