rbiNormGibbs             package:bayesm             R Documentation

_I_l_l_u_s_t_r_a_t_e _B_i_v_a_r_i_a_t_e _N_o_r_m_a_l _G_i_b_b_s _S_a_m_p_l_e_r

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

     'rbiNormGibbs' implements a Gibbs Sampler for the bivariate normal
     distribution. Intermediate moves are shown and the output is
     contrasted with the iid sampler. i This function is designed for
     illustrative/teaching purposes.

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

     rbiNormGibbs(initx = 2, inity = -2, rho, burnin = 100, R = 500)

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

   initx: initial value of parameter on x axis (def: 2) 

   inity: initial value of parameter on y axis (def: -2)  

     rho: correlation for bivariate normals 

  burnin: burn-in number of draws (def:100)  

       R: number of MCMC draws (def:500) 

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

     (theta1,theta2) ~ N((0,0), Sigma=matrix(c(1,rho,rho,1),ncol=2))

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

     R x 2 array of draws

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

     Peter Rossi, Graduate School of Business, University of Chicago,
     Peter.Rossi@ChicagoGsb.edu.

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

     For further discussion, see _Bayesian Statistics and Marketing_ by
     Rossi, Allenby and McCulloch, Chapters 2 and 3. 
      <URL:
     http://gsbwww.uchicago.edu/fac/peter.rossi/research/bsm.html>

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

     ##
     ## Not run:  out=rbiNormGibbs(rho=.95) 

