A Bayesian hyperbolic latent space model with an inferred temperature parameter outperforms fixed-temperature and Euclidean alternatives in network reconstruction on simulated and real data.
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AGESS combines elliptical slice sampling with diminishing adaptation to self-correct to fast mixing while preserving ergodicity for non-elliptical, multi-modal, and high-dimensional targets.
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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Hyperbolic Latent Space Models for Network Embedding: Model Specification and Bayesian Inference
A Bayesian hyperbolic latent space model with an inferred temperature parameter outperforms fixed-temperature and Euclidean alternatives in network reconstruction on simulated and real data.
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Adaptive Generalized Elliptical Slice Sampling
AGESS combines elliptical slice sampling with diminishing adaptation to self-correct to fast mixing while preserving ergodicity for non-elliptical, multi-modal, and high-dimensional targets.
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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.