Fully Bayesian Forecasts with Evidence Networks

2023/09/13 by Thomas Gessey-Jones, Will Handley, Gessey-Jones, T. +1 · 1 citation
Computer Science · Decision Sciences · Mathematics · #Bayesian Modeling and Causal Inference #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Forecasting Techniques and Applications #General Relativity and Quantum Cosmology (gr-qc) #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.48550/arxiv.2309.06942

openalex publication_date 2023/09/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Sensitivity forecasts inform the design of experiments and the direction of theoretical efforts. To arrive at representative results, Bayesian forecasts should marginalize their conclusions over uncertain parameters and noise realizations rather than picking fiducial values. However, this is typically computationally infeasible with current methods for forecasts of an experiment's ability to distinguish between competing models. We thus propose a novel simulation-based methodology capable of providing expedient and rigorous Bayesian model comparison forecasts without relying on restrictive assumptions.

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