logdensity_admkr: Calculate an estimate of log posterior ordinate used in the...
In bbemkr: Bayesian bandwidth estimation for multivariate kernel regression with Gaussian error

Description Usage Arguments Details Value Author(s) References See Also

View source: R/logdensity_admkr.R View source: R/logdensity_admkr.R

Log marginal likelihood = Log likelihood + Log prior - Log density

1	logdensity_admkr(tau2, cpost)

`tau2`	Square of re-parameterized bandwidths and square of normal error variance
`cpost`	Simulation output of `tau2` obtained from the MCMC iterations

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. Series B, 56(1), 3-48.

logpriors_admkr, loglikelihood_admkr, mcmcrecord_admkr

bbemkr documentation built on May 1, 2019, 10:53 p.m.