logpriors_admkr: Compute the marginal likelihood using Chib's (1995) method
In bbefkr: Bayesian bandwidth estimation and semi-metric selection for the functional kernel regression with unknown error density

The log marginal likelihood can be computed as the log likelihood + log prior - log posterior. The latter one can be estimated from the MCMC.

1	logpriors_admkr(h2, prior_alpha, prior_beta)

`h2`	Squared bandwidths
`prior_alpha`	Hyper-parameter of the inverse gamma distribution
`prior_beta`	Hyper-parameter of the inverse gamma distribution

The value of log prior density in the marginal likelihood

Han Lin Shang

S. Chib (1995) ‘Marginal likelihood from the Gibbs output’, Journal of the American Statistical Association, 90(432), 1313-1321.

bbefkr documentation built on May 2, 2019, 3:04 a.m.

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