UseBasicPrior           package:vabayelMix           R Documentation

_P_r_i_o_r _F_u_n_c_t_i_o_n _f_o_r _V_a_r_i_a_t_i_o_n_a_l _G_a_u_s_s_i_a_n _M_i_x_t_u_r_e _M_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     This function implements an uninformative prior distribution for
     the cluster centers and variances, but allows the user to define
     prior weights for the clusters.

_U_s_a_g_e:

     UseBasicPrior(data, weights.v)

_A_r_g_u_m_e_n_t_s:

    data: A matrix with columns representing variables and rows
          observations. Algorithm clusters observations.

weights.v: A vector of relative prior weights for the clusters. 

_D_e_t_a_i_l_s:

     'weights.v' is a vector of length 'Ncat', the maximum number of
     clusters to look for.

_V_a_l_u_e:

     A list with following components. The first four are matrices of
     dimension 'Ncat' x 'Ndim', 'dapi' is a vector of length 'Ncat'. 

    mean: the means of the cluster mean gaussian priors.

    varm: the inverse variances for the cluster mean gaussian priors.

   ivara: parameters for the gamma prior distribution of the inverse
          variances of the clusters. See references.

   ivarb: parameters for the gamma prior distribution of the inverse
          variances of the clusters. See references.

    dapi: weight vector specifying prior knowledge about the number of
          clusters.

_A_u_t_h_o_r(_s):

     Andrew Teschendorffaet21@hutchison-mrc.cam.ac.uk

_R_e_f_e_r_e_n_c_e_s:

_1 D.J.MacKay: Developments in probabilistic modelling with neural
     networks-ensemble learning. In Neural Networks: Artificial
     Intelligence and Industrial Applications. Proceedings of the 3rd
     Annual Symposium on Neural Networksm Nijmengen, Netherlands,
     Berlin Springer, 191-198 (1995).

_2 J.W.Miskin : Ensemble Learning for Independent Component Analysis,
     PhD thesis University of Cambridge December 2000.

_3 A. E. Teschendorff,...et al.: A variational bayesian mixture
     modelling framework for cluster analysis of gene expression data.
     Submitted to Bioinformatics.

