logpriors_gaussian {bbemkr} | R Documentation |
Log marginal likelihood = Log likelihood + Log prior - Log density
logpriors_gaussian(h2, data_x, prior_p, prior_st)
h2 |
Square of re-parameterized bandwidths and square of normal error variance |
data_x |
Regressors |
prior_p |
Hyperparameter used in the inverse-gamma prior |
prior_st |
Hyperparameter used in the inverse-gamma prior |
Calculate the log prior using the estimated averaged bandwidths of the regressors and the estimated averaged variance of the error density, obtained from the MCMC iterations
Value of the log prior
Han Lin Shang
S. Chib and I. Jeliazkov (2001) Marginal likelihood from the Metropolis-Hastings output, Journal of the American Statistical Association, 96, 453, 270-281.
S. Chib (1995) Marginal likelihood from the Gibbs output, Journal of the American Statistical Association, 90, 432, 1313-1321.
M. A. Newton and A. E. Raftery (1994) Approximate Bayesian inference by the weighted likelihood bootstrap (with discussion), Journal of the Royal Statistical Society, 56, 3-48.
logdensity_gaussian
, loglikelihood_gaussian
, mcmcrecord_gaussian