logpriors_gaussian {bbemkr}R Documentation

Calculate the log prior used in the log marginal density of Chib (1995).

Description

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

Usage

logpriors_gaussian(h2, data_x, prior_p, prior_st)

Arguments

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

Details

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

Value of the log prior

Author(s)

Han Lin Shang

References

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.

See Also

logdensity_gaussian, loglikelihood_gaussian, mcmcrecord_gaussian


[Package bbemkr version 2.0 Index]