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Amortised and provably-robust simulation-based inference
Complex simulator-based models are now routinely used to perform inference across the sciences and engineering, but existing inference …
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BayesSum: Bayesian Quadrature in Discrete Spaces
This paper addresses the challenging computational problem of estimating intractable expectations over discrete domains. Existing …
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Conjugate Generalised Bayesian Inference for Discrete Doubly Intractable Problems
Doubly intractable problems occur when both the likelihood and the posterior are available only in unnormalised form, with …
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Multi-Output Robust and Conjugate Gaussian Processes
Multi-output Gaussian process (MOGP) regression allows modelling dependencies among multiple correlated response variables. Similarly …
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Stationary MMD Points
Approximation of a target probability distribution using a finite set of points is a problem of fundamental importance in numerical …
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A Dictionary of Closed-Form Kernel Mean Embeddings
Kernel mean embeddings – integrals of a kernel with respect to a probability distribution – are essential in Bayesian …
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Approximate Top-k for Increased Parallelism
We present an evaluation of bucketed approximate top-k algorithms. Computing top-k exactly suffers from limited parallelism, because …
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Targeted Separation and Convergence with Kernel Discrepancies
Maximum mean discrepancies (MMDs) like the kernel Stein discrepancy (KSD) have grown central to a wide range of applications, including …
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Predictive performance of power posteriors
We analyse the impact of using tempered likelihoods in the production of posterior predictions. Our findings reveal that once the …
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