logpoissnormal          package:LearnBayes          R Documentation

_L_o_g  _p_o_s_t_e_r_i_o_r _w_i_t_h _P_o_i_s_s_o_n _s_a_m_p_l_i_n_g _a_n_d _n_o_r_m_a_l _p_r_i_o_r

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

     Computes the logarithm of the posterior density of a Poisson log
     mean with a normal prior

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

     logpoissnormal(theta,datapar)

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

   theta: vector of values of the log mean parameter

 datapar: list with components data, vector of observations, and par,
          vector of parameters of the normal prior

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

     vector of values of the log posterior for all values in theta

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

     Jim Albert

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

     data=c(2,4,3,6,1,0,4,3,10,2)
     par=c(0,1)
     datapar=list(data=data,par=par)
     theta=c(-1,0,1,2)
     logpoissnormal(theta,datapar)

