DPdensity之学习.doc

DPdensity {DPpackage} R Documentation Semiparametric Bayesian density estimation using a DPM of normals Description This function generates a posterior density sample for a Dirichlet process mixture of normals model. Usage DPdensity(y,ngrid=1000,grid=NULL,prior,mcmc,state,status, method=neal,data=sys.frame(sys.parent()), na.action=na.fail) Arguments y a vector or matrix giving the data from which the density estimate is to be computed. ngrid number of grid points where the density estimate is evaluated. This is only used if dimension of y is lower or equal than低于或等于 2. The default value is 1000. grid matrix of dimension ngrid*nvar of grid points where the density estimate is evaluated. This is only used if dimension of y is lower or equal than 2. The default value缺省值 is NULL and the grid is chosen according to the range of the data. prior a list giving the prior information. The list includes the following parameter: a0 and b0 giving the hyperparameters for prior distribution of the precision parameter of the Dirichlet process prior, alpha giving the value of the precision parameter (it must be specified if a0 is missing, see details below), nu2 and psiinv2 giving the hyperparameters of the inverted Wishart prior distribution for the scale matrix, Psi1, of the inverted Wishart part of the baseline distribution, tau1 and tau2 giving the hyperparameters for the gamma prior distribution of the scale parameter k0 of the normal part of the baseline distribution, m2 and s2 giving the mean and the covariance of the normal prior for the mean, m1, of the normal component of the baseline distribution, respectively, nu1 and psiinv1 (it must be specified if nu2 is missing, see details below) giving the hyperparameters of the inverted Wishart part of the baseline distribution and, m1 giving the mean of the normal part of the baseline distribution (it must be specified if m2 is missing, see details below) and, k0 giving the scale parameter of the no