groupeddatapost          package:LearnBayes          R Documentation

_L_o_g _p_o_s_t_e_r_i_o_r _o_f _n_o_r_m_a_l _p_a_r_a_m_e_t_e_r_s _w_h_e_n _d_a_t_a _i_s _i_n _g_r_o_u_p_e_d _f_o_r_m

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

     Computes the log posterior density of (M,log S) for normal
     sampling where the data is observed in grouped form

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

     groupeddatapost(theta,data)

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

   theta: matrix of parameter values where each row represents a value
          of (M, log S)

    data: list with components b, a vector of midpoints and f, the
          corresponding bin frequencies

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

     vector of values of the log posterior where each value corresponds
     to each row of the parameters in theta

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

     Jim Albert

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

     b=c(10,15,20,25,30)
     f=c(2,5,8,4,2)
     data=list(b=b,f=f)
     par1=c(20,1)
     par2=c(22,0)
     theta=rbind(par1,par2)
     groupeddatapost(theta,data)

