loglikelihood_gaussian {bbemkr}R Documentation

Calculate the log likelihood used in the Chib's (1995) log marginal density

Description

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

Usage

loglikelihood_gaussian(h2, data_x, data_y)

Arguments

h2

Square of re-parameterized bandwidths and square of normal error variance

data_x

Regressors

data_y

Response

Details

Calculates the log likelihood using the estimated averaged bandwidths of the regressors and estimated averaged variance of the error density

Value

The value of log likelihood, with parameters (bandwidths + normal error variance) estimated from the MCMC iterations

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

logpriors_gaussian, logdensity_gaussian, mcmcrecord_gaussian


[Package bbemkr version 2.0 Index]