loglikelihood_gaussian: Calculate the log likelihood used in the Chib's (1995) log...
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	loglikelihood_gaussian(h2, data_x, data_y)

`h2`	Square of re-parameterized bandwidths and square of normal error variance
`data_x`	Regressors
`data_y`	Response

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

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

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, logdensity_gaussian, mcmcrecord_gaussian

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