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Accelerating pseudo marginal MCMC using Gaussian processes

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Figure

Table 2: Average run times for GP-GIMH (excluding the MCWM pre-computing phase andGP fitting) for different combinations of J and ϵ for the stochastic volatility example
Table 4: Upper triangular part of the posterior correlation matrix for θ obtained from thegold standard GIMH/GP-GIMH (first run with J ≈ 2000 and ϵ = 1.2) algorithms for thegene network model.
Table 5: Average run times for GP-GIMH (excluding the MCWM pre-computing phase andGP fitting) for different combinations of J and ϵ for the gene network example
Figure 7: Estimated marginal posterior densities for the gene network example from GIMHwith σ = 0.6 (red dash) and GP-GIMH with σ = 0.4 and a burn-in phase of B = 20000iterations (black solid).
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