loglikelihood_gaussian {bbemkr} | R Documentation |
Log marginal likelihood = Log likelihood + Log prior - Log density
loglikelihood_gaussian(h2, data_x, data_y)
h2 |
Square of re-parameterized bandwidths and square of normal error variance |
data_x |
Regressors |
data_y |
Response |
Calculates the log likelihood using the estimated averaged bandwidths of the regressors and estimated averaged variance of the error density
The value of log likelihood, with parameters (bandwidths + normal error variance) estimated from the MCMC iterations
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.
logpriors_gaussian
, logdensity_gaussian
, mcmcrecord_gaussian