logdensity_gaussian {bbemkr} | R Documentation |
Log marginal likelihood = Log likelihood + Log prior - Log density
logdensity_gaussian(tau2, cpost)
tau2 |
Square of re-parameterized bandwidths and square of normal error variance |
cpost |
Simulation output of |
It should be noted that the posterior mode or maximum likelihood estimate can be computed from the simulation output at least approximately, if it is easy to evaluate the log-likelihood function for each draw in the simulation. Alternatively, one can make use of the posterior mean provided that there is no concern that it is a low density point.
Value of the log density
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
, loglikelihood_gaussian
, mcmcrecord_gaussian