The paper extends Neural Posterior Estimation (NPE) to handle simulators whose parameter spaces contain both discrete and continuous dimensions. It introduces an inference network that factorizes the joint posterior into discrete and continuous components, using an autoregressive classifier for the discrete part and a generative model for the continuous part, trained jointly with a single simulation-based objective. A diagnostic tool for assessing calibration of the mixed posterior is also proposed, and the method is shown to produce accurate, calibrated posteriors on toy and real scientific simulators.
By Jan Boelts, Cornelius Schr\"oder, Jonas Beck, Jakob H. Macke, Michael Deistler, Daniel Gedon
arXiv:2607. 27320v1 Announce Type: cross Abstract: Field-level inference of cosmological initial conditions from galaxy surveys requires a forward model that is simultaneously accurate in the non-linear regime, computationally efficient, and fully differentiable.
By Cooper Jacobus, Beatriz Tucci, Oliver Philcox
arXiv:2607. 29389v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated a strong ability to generate syntactically correct code from natural-language specifications.
By Jan Marius St\"urmer, Jascha Knack, Tobias Koch, Andreas Weinmann
arXiv:2602. 18266v2 Announce Type: replace Abstract: Automated methods for discovering mechanistic simulator models from observational data offer a promising path toward accelerating scientific progress.
By Stefan Wahl, Raphaela Schenk, Ali Farnoud, Jakob H. Macke, Daniel Gedon
arXiv:2609.36044v1 Announce Type: cross
Abstract: We develop two hybrid techniques that approach the performance of neural simulation-based inference (NSBI) analyses while substantially reducing the...
By Sean Benevedes, Mani Dehghan, Aishik Ghosh, Tae Hyoun Park
arXiv:2605.11280v2 Announce Type: replace-cross
Abstract: Fast surrogate models for expensive simulations are now essential across the sciences, yet they typically operate as black boxes. We present...
By Tousif Islam, Digvijay Wadekar, Tejaswi Venumadhav, Matias Zaldarriaga, Ajit Kumar Mehta, Javier Roulet, Barak Zackay
arXiv:2407. 20432v3 Announce Type: replace Abstract: Bayesian inference methods such as Markov Chain Monte Carlo (MCMC) typically require repeated computations of the likelihood function, but in some scenarios this is infeasible and alternative methods are needed.
By Linnea M Wolniewicz, Peter Sadowski, Claudio Corti
arXiv:2608. 13774v1 Announce Type: new Abstract: Markov chain Monte Carlo (MCMC) requires only the ability to evaluate the likelihood, making it a common technique for inference in complex models.
By Harini Venkatesan, Christian Shelton, Ming-Feng Ho, Simeon Bird, Mengxuan Wu
SimCast‑S2S is a generative latent‑diffusion model designed for probabilistic subseasonal‑to‑seasonal precipitation forecasting. It tackles three key challenges: it uses a diffusion pipeline to capture uncertainty, operates in a compact latent space to enable efficient large‑ensemble generation, and leverages transfer learning with low‑rank adaptation to train on limited reanalysis data after pretraining on climate simulations. The model outperforms deep‑learning baselines and competes with, or surpasses, operational systems such as the ECMWF‑S2S baseline without requiring extensive post‑processing.
By Hiep V. Dang, Antonios Mamalakis
arXiv:2606. 10023v1 Announce Type: cross Abstract: Accurate posterior estimation is central to scientific inference, as uncertainties determine what can be reliably learned from observational data.
By Ludvig Doeser, Jens Jasche
arXiv:2603. 22006v2 Announce Type: replace-cross Abstract: Upcoming stage-IV surveys such as Euclid and Rubin will deliver vast amounts of high-precision data, opening new opportunities to constrain cosmological models with unprecedented accuracy.
By Hubert Leterme, Andreas Tersenov, Jalal Fadili, Jean-Luc Starck
arXiv:2606.23346v2 Announce Type: replace-cross
Abstract: We perform a realistic KiDS-Legacy mock analysis with field-level neural compression and simulation-based inference using just 60 $N$-body si...
By Alex A. Saoulis, Kiyam Lin, Niall Jeffrey, Maximilian von Wietersheim-Kramsta, Davide Piras, Alessio Spurio Mancini, Ana M. G. Ferreira, Benjamin Joachimi