np_gibbs(xh, inicost, k, mutsizp, prob, data_x, data_y, prior_p, prior_st)accept_h=1 indicates acceptance, while accept_h=0 indicates rejection.2) Conditioning on the variance parameter of the error density, we implement random-walk Metropolis algorithm to update the bandwidths, in order to achieve the minimum cost value.
3) The variance of the error density can be directly sampled.
4) Iterate steps 2) and 3) until the cost value is minimized.
5) Check the convergence of the parameters by examining the simulation inefficient factor (sif) value. The smaller the sif value is, the better convergence of the parameters is.
B. W. Silverman (1986) Density Estimation for Statistics and Data Analysis. Chapman and Hall, New York.
mcmcrecord_gaussian, logdensity_gaussian, loglikelihood_gaussian, logpriors_gaussian