gibbsmix                package:mcsm                R Documentation

_I_m_p_l_e_m_e_n_t_a_t_i_o_n _o_f _a _G_i_b_b_s _s_a_m_p_l_e_r _o_n _a _m_i_x_t_u_r_e _p_o_s_t_e_r_i_o_r

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

     This function runs a standard Gibbs sampling algorithm on a
     posterior distribution associated with a mixture model and 500
     datapoints.

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

     gibbsmix(Niter = 10^4, v = 1)

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

   Niter: Number of MCMC iterations

       v: Scale of the normal prior

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

     The function returns a plot of the log-posterior surface, along
     with the MCMC sample represented both by points and lines linking
     one value to the next. Evaluating the log-posterior surface on a
     250x250 grid takes some time.

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

     Christian P. Robert and George Casella

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

     Chapter 6 of *EnteR Monte Carlo Statistical Methods*

_E_x_a_m_p_l_e_s:

     ## Not run: gibbsmix(Nit=10^3)

