Year: 2024
Type: preprint
Abstract: Identifying the parameters of a non-linear model that best explain observed data is a core task across scientific fields. When such models rely on complex simulators, evaluating the likelihood is typically intractable, making traditional inference methods such as MCMC inapplicable. Simulation-based inference (SBI) addresses this by training deep generative models to approximate the posterior distribution over parameters using simulated data. In this work, we consider the tall data setting, where multiple independent observations provide additional information, allowing sharper posteriors and improved parameter identifiability. Building on the flourishing score-based diffusion literature, F-NPSE (Geffner et al., 2023) estimates the tall data posterior by composing individual scores from a neural network trained only for a single context observation. This enables more flexible and simulation-efficient inference than alternative approaches for tall datasets in SBI. However, it relies on costly Langevin dynamics during sampling. We propose a new algorithm that eliminates the need for Langevin steps by explicitly approximating the diffusion process of the tall data posterior. Our method retains the advantages of compositional score-based inference while being significantly faster and more stable than F-NPSE. We demonstrate its improved performance on toy problems and standard SBI benchmarks, and showcase its scalability by applying it to a complex real-world model from computational neuroscience.
Source: arXiv (Cornell University)
Institutions: Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Université Paris-Saclay, CEA Paris-Saclay, MIND: modèles et inférence pour les données de Neuroimagerie, Centre de Mathématiques Appliquées de l'École polytechnique, Institut Polytechnique de Paris, École Polytechnique, Sorbonne Université, Laboratoire de Probabilités, Statistique et Modélisation, Institut polytechnique de Grenoble, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Laboratoire Jean Kuntzmann, STATIFY: Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
OpenAlex ID: W4391916262