logpriors_gaussian: Calculate the log prior used in the log marginal density of...
In bbemkr: Bayesian bandwidth estimation for multivariate kernel regression with Gaussian error

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

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

1	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

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