We introduce here a novel sample-efficient inference framework, V ariational Bayesian Monte Carlo (VBMC). VBMC combines variational inference with Gaussian-process based, active-sampling Bayesian quadrature, using the latter to efficiently approximate the intractable integral in the variational objective.

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Now, since as mentioned above the KL divergence is a non-negative quantity, from Eq. S2 we have

We introduce new'global' acquisition functions, such as expected

We thank the reviewers for their useful and thoughtful feedback. We are glad to see that our work was found " highly That is, " the experiments show that the proposed method outperforms the baselines and We address the reviewers' comments below and ' we refer to our method for noisy inference. " How does the estimation error [of Will it change the claims of the paper? Thus, our claims are unaffected. While we share the reviewers' desire for convergence guarantees, we also note that Thus, we go from Eq. 7 to Eq. 8 by swapping Re. the noiseless case, see Figure 1 In Eq. 7, the prior density is included in The ablation studies are mentioned in the text, and fully reported in the Supplement (E.3, 'Lesion study').

Now, since as mentioned above the KL divergence is a non-negative quantity, from Eq. S2 we have

We thank the reviewers for their useful and thoughtful feedback. We are glad to see that our work was found " highly That is, " the experiments show that the proposed method outperforms the baselines and We address the reviewers' comments below and ' we refer to our method for noisy inference. " How does the estimation error [of Will it change the claims of the paper? Thus, our claims are unaffected. While we share the reviewers' desire for convergence guarantees, we also note that Thus, we go from Eq. 7 to Eq. 8 by swapping Re. the noiseless case, see Figure 1 In Eq. 7, the prior density is included in The ablation studies are mentioned in the text, and fully reported in the Supplement (E.3, 'Lesion study').

Variational Bayesian Monte Carlo (VBMC) is a sample-efficient method for approximate Bayesian inference with computationally expensive likelihoods. While VBMC's local surrogate approach provides stable approximations, its conservative exploration strategy and limited evaluation budget can cause it to miss regions of complex posteriors. In this work, we introduce Stacking Variational Bayesian Monte Carlo (S-VBMC), a method that constructs global posterior approximations by merging independent VBMC runs through a principled and inexpensive post-processing step. Our approach leverages VBMC's mixture posterior representation and per-component evidence estimates, requiring no additional likelihood evaluations while being naturally parallelizable. We demonstrate S-VBMC's effectiveness on two synthetic problems designed to challenge VBMC's exploration capabilities and two real-world applications from computational neuroscience, showing substantial improvements in posterior approximation quality across all cases.

Variational Bayesian Monte Carlo (VBMC) is a recently introduced framework that uses Gaussian process surrogates to perform approximate Bayesian inference in models with black-box, non-cheap likelihoods. In this work, we extend VBMC to deal with noisy log-likelihood evaluations, such as those arising from simulation-based models. We introduce new global' acquisition functions, such as expected information gain (EIG) and variational interquantile range (VIQR), which are robust to noise and can be efficiently evaluated within the VBMC setting. In a novel, challenging, noisy-inference benchmark comprising of a variety of models with real datasets from computational and cognitive neuroscience, VBMC VIQR achieves state-of-the-art performance in recovering the ground-truth posteriors and model evidence.