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Gaussian processes allow marginalizing the latent function to obtain a marginal likelihood.
The focus is on two methods of marginal likelihood estimation.
Explores how typical methods (reinforcement learning and maximum marginal likelihood) overfit spurious programs.
And focusing only in the marginal likelihood that you have for that parameter.
From language to programs: bridging reinforcement learning and maximum marginal likelihood.
For the maximization of the penalized marginal likelihood the EM-algorithm is adapted.
Such poles are seen as hyperparameters which are tuned via marginal likelihood optimization.
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models.
And then you can solve for the marginal likelihood and the uncertainty in that one parameter that you're interested in.
Recently, the nested sampling estimator (NSE) has been widely used as an efficient method to estimate the marginal likelihood.
Overparameterization is avoided because the model involves only a few hyperparameters that are tuned via marginal likelihood maximization.
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