Estimation of model marginal likelihoods for Bayesian model selection using iterative kernel density estimation. A multitude of methods exist for performing model selection in general and estimating marginal likelihood in specific, but none are partically well-suited to large models (such as Gaussian processes) applied to relatively limited datasets. Methods are provided to specific and construct Stan models, estimate those models, and estimate the marginal likelihood of those models.
|Maintainer||Taylor McKenzie <[email protected]>|
|Package repository||View on GitHub|
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