Scalable Simulation-Based Model Inference with Test-Time Complexity Control

• URL: http://arxiv.org/abs/2603.15292v1
• Date: Mon, 16 Mar 2026 13:54:15 GMT
• Title: Scalable Simulation-Based Model Inference with Test-Time Complexity Control
• Abstract summary: PRISM is a simulation-based encoder-decoder that infers a joint posterior over both discrete model structures.<n>We evaluate PRISM on biophysical modeling for diffusion MRI data, showing the ability to perform model selection across several multi-compartment models.
• Score: 12.86778906492369
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulation plays a central role in scientific discovery. In many applications, the bottleneck is no longer running a simulator; it is choosing among large families of plausible simulators, each corresponding to different forward models/hypotheses consistent with observations. Over large model families, classical Bayesian workflows for model selection are impractical. Furthermore, amortized model selection methods typically hard-code a fixed model prior or complexity penalty at training time, requiring users to commit to a particular parsimony assumption before seeing the data. We introduce PRISM, a simulation-based encoder-decoder that infers a joint posterior over both discrete model structures and associated continuous parameters, while enabling test-time control of model complexity via a tunable model prior that the network is conditioned on. We show that PRISM scales to families with combinatorially many (up to billions) of model instantiations on a synthetic symbolic regression task. As a scientific application, we evaluate PRISM on biophysical modeling for diffusion MRI data, showing the ability to perform model selection across several multi-compartment models, on both synthetic and in vivo neuroimaging data.

Related papers

• Program Synthesis for Simulation-Based Inference: Joint Model Selection and Parameter Estimation [0.038073142980733]
  We present a framework for joint model selection and parameter estimation that combines large language models for program synthesis with neural simulation-based inference.<n>The approach enables simulation-based inference over a pool of models, not just parameters within a fixed model.
  arXiv  Detail & Related papers  (2026-07-20T04:33:35Z)
• Quantifying and Attributing Submodel Uncertainty in Stochastic Simulation Models and Digital Twins [0.1234398109349733]
  This paper investigates how submodel uncertainty affects the estimation of system performance metrics.<n>We develop a framework for quantifying submodel uncertainty in simulation models and extend the framework to digital-twin settings.
  arXiv  Detail & Related papers  (2026-02-18T00:06:39Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• Simultaneous identification of models and parameters of scientific simulators [7.473394133229206]
  We develop a simulation-based inference framework to identify essential model components. It can be applied to any compositional simulator without requiring evaluations. It reveals non-identifiable model components and parameters.
  arXiv  Detail & Related papers  (2023-05-24T14:06:02Z)
• Deep incremental learning models for financial temporal tabular datasets with distribution shifts [0.9790236766474201]
  The framework uses a simple basic building block (decision trees) to build self-similar models of any required complexity. We demonstrate our scheme using XGBoost models trained on the Numerai dataset and show that a two layer deep ensemble of XGBoost models over different model snapshots delivers high quality predictions.
  arXiv  Detail & Related papers  (2023-03-14T14:10:37Z)
• Conditional Generative Models for Simulation of EMG During Naturalistic Movements [45.698312905115955]
  We present a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy.
  arXiv  Detail & Related papers  (2022-11-03T14:49:02Z)
• Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference [0.6524460254566905]
  We show that deep sequence-to-sequence (seq2seq) models can serve as accurate surrogates for complex epidemic models. Once trained, our surrogate can predict scenarios a several thousand times faster than the original model.
  arXiv  Detail & Related papers  (2022-09-20T11:23:19Z)
• Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose? [0.2836066255205732]
  We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models. We find that on an environment that requires multimodal posterior predictives, mixture density nets outperform all other models by a large margin. We also found that deterministic models are on par, in fact they consistently (although non-significantly) outperform their probabilistic counterparts.
  arXiv  Detail & Related papers  (2021-07-24T11:38:25Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Hybrid modeling: Applications in real-time diagnosis [64.5040763067757]
  We outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models. We are using such models for real-time diagnosis applications.
  arXiv  Detail & Related papers  (2020-03-04T00:44:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.