A determinant-free, gradient-based HMC sampler for fully Bayesian GPR that uses the pseudofermion trick and pole expansions to avoid the kernel determinant.
Hogg, and Michael O’Neil
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A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression
A determinant-free, gradient-based HMC sampler for fully Bayesian GPR that uses the pseudofermion trick and pole expansions to avoid the kernel determinant.