MembProbFn2            package:vabayelMix            R Documentation

_I_n_t_e_r_n_a_l _f_u_n_c_t_i_o_n _f_o_r: _V_a_r_i_a_t_i_o_n_a_l _B_a_y_e_s_i_a_n _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:

     Computes cluster membership probabilities of of samples.

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

     MembProbFn2(data, NewVals, Nsamples)

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

    data: The data matrix

 NewVals: Estimated parameter values

Nsamples: Number of samples

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

     A list with the following components: 

     wcl: Integer vector of length Nsamples specifying cluster
          membership of sample using maximum probabilty criterion

   probs: Matrix of dimension Ncat x Nsamples giving cluster membership
          probabilities of samples.

_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.

