Proposes an adaptive generalized elliptical slice sampling algorithm that improves efficiency on non-elliptical, non-differentiable, multi-modal and high-dimensional targets and proves ergodicity under general regularity conditions.
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UNVERDICTED 3representative citing papers
A Bayesian hyperbolic latent space model with inferable temperature parameter outperforms fixed-temperature and Euclidean models in network reconstruction by better capturing tree-like topologies.
Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.
citing papers explorer
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Adaptive Generalized Elliptical Slice Sampling
Proposes an adaptive generalized elliptical slice sampling algorithm that improves efficiency on non-elliptical, non-differentiable, multi-modal and high-dimensional targets and proves ergodicity under general regularity conditions.
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Hyperbolic Latent Space Models for Network Embedding: Model Specification and Bayesian Inference
A Bayesian hyperbolic latent space model with inferable temperature parameter outperforms fixed-temperature and Euclidean models in network reconstruction by better capturing tree-like topologies.
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Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference
Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.