Simulation-based parameter estimation via a combination of embedded normalizing flows and implied empirical probabilities under moment restrictions

• URL: http://arxiv.org/abs/2607.25026v1
• Date: Mon, 27 Jul 2026 19:36:51 GMT
• Title: Simulation-based parameter estimation via a combination of embedded normalizing flows and implied empirical probabilities under moment restrictions
• Abstract summary: We present a simulation-based parameter estimation framework for a model defined by a computational simulation of a physical system.<n>We specifically outline an estimation framework consisting of two closely-integrated steps that facilitate an overall end-to-end parameter estimation scheme.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we present a simulation-based parameter estimation framework for a model defined by a computational simulation of a physical system. We specifically outline an estimation framework consisting of two closely-integrated steps that facilitate an overall end-to-end parameter estimation scheme. The first step involves utilizing an embedded normalizing flow which is used to transform the unknown complex distribution of the residual information into a simple base distribution corresponding to the transformed residual information. In the second step, an empirical-likelihood estimator, under moment restrictions, is utilized for imposing an indirect constrain on the base distribution, where such an instantiated task reasonably allows us to treat the transformed residual information as random variables arising from discretely distribution population with each transformed data point as a single-cell from a set of finite-cell contingencies. Moreover, we use first-order gradient methods for updating the estimated parameter values of the model defined by the computational simulation and the corresponding parametrized embedded normalizing flow, that call for all gradient-related information by leveraging implicitly differentiations of the empirical-likelihood function, which is constructed from the implied empirical probabilities under moment restrictions. Here, it is worth mentioning that the problem formulation presented in this work, which highlights an information-theoretic interpretation, allows to present a computational framework for algorithmic implementations. Finally, as a-by-product, the inverse of the parametrized embedded normalizing flow, w.r.t. the estimated parameter values, serves as a surrogate model for the computational simulation model, which provides useful information for quantifying model discrepancies and sensitivity analysis.

Related papers

• Non-Parametric Rehearsal Learning via Conditional Mean Embeddings [88.89267783967263]
  We propose a non-parametric rehearsal learning approach for textitavoiding undesired future (AUF) problems.<n>Specifically, we use kernel machinery to reformulate the AUF objective into a unified representation that disentangles desirability modeling from action-induced distributional changes.
  arXiv  Detail & Related papers  (2026-05-09T15:30:52Z)
• Profiling systematic uncertainties in Simulation-Based Inference with Factorizable Normalizing Flows [0.0]
  We propose a general framework for Simulation-Based Inference that efficiently profiles nuisance parameters.<n>We introduce Factorizable Normalizing Flows to model systematic variations as a parametrics of a nominal density.<n>We develop an amortized training strategy that learns the conditional dependence of the DoI on nuisance parameters in a single optimization process.<n>This allows for the simultaneous extraction of the underlying distribution and the robust profiling of nuisances.
  arXiv  Detail & Related papers  (2026-02-13T18:48:12Z)
• Simulation-Based Inference via Regression Projection and Batched Discrepancies [1.9435397960631862]
  We analyze a lightweight simulation-based inference method that infers simulator parameters using only a regression-based projection of the observed data.<n> Experiments on a tractable nonlinear model and on a cosmological calibration task using the DREAMS simulation suite illustrate the computational advantages of regression-based projections.
  arXiv  Detail & Related papers  (2026-02-03T15:07:40Z)
• Efficient Covariance Estimation for Sparsified Functional Data [51.69796254617083]
  proposed Random-knots (Random-knots-Spatial) and B-spline (Bspline-Spatial) estimators of the covariance function are computationally efficient.<n>Asymptotic pointwise of the covariance are obtained for sparsified individual trajectories under some regularity conditions.
  arXiv  Detail & Related papers  (2025-11-23T00:50:33Z)
• Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis) [55.2480439325792]
  This thesis is a series of independent contributions to statistics unified by a model-free perspective.<n>The first chapter elaborates on how a model-free perspective can be used to formulate flexible methods that leverage prediction techniques from machine learning.<n>The second chapter studies the concept of local independence, which describes whether the evolution of one process is directly influenced by another.
  arXiv  Detail & Related papers  (2025-02-11T19:24:09Z)
• Amortized In-Context Bayesian Posterior Estimation [15.714462115687096]
  Amortization, through conditional estimation, is a viable strategy to alleviate such difficulties.<n>We conduct a thorough comparative analysis of amortized in-context Bayesian posterior estimation methods.<n>We highlight the superiority of the reverse KL estimator for predictive problems, especially when combined with the transformer architecture and normalizing flows.
  arXiv  Detail & Related papers  (2025-02-10T16:00:48Z)
• Probabilistic Shapley Value Modeling and Inference [4.025747321359554]
  Probability Shapley inference (PSI) is a novel framework to model and infer sufficient statistics of feature attributions in flexible predictive models.<n>We introduce a masking-based neural network architecture, with a modular training and inference procedure.<n>We evaluate PSI on synthetic and real-world datasets, showing that it achieves competitive predictive performance compared to strong baselines.
  arXiv  Detail & Related papers  (2024-02-06T18:09:05Z)
• Conditional Mean and Variance Estimation via \ extit{k}-NN Algorithm with Automated Variance Selection [9.943131787772323]
  We introduce a novel textitk-nearest neighbor (textitk-NN) regression method for joint estimation of the conditional mean and variance.<n>The proposed algorithm preserves the computational efficiency and manifold-learning capabilities of classical non-parametric textitk-NN models.
  arXiv  Detail & Related papers  (2024-02-02T18:54:18Z)
• Maximum Likelihood Learning of Unnormalized Models for Simulation-Based Inference [44.281860162298564]
  We introduce two synthetic likelihood methods for Simulation-Based Inference. We learn a conditional energy-based model (EBM) of the likelihood using synthetic data generated by the simulator. We demonstrate the properties of both methods on a range of synthetic datasets, and apply them to a model of the neuroscience network in the crab.
  arXiv  Detail & Related papers  (2022-10-26T14:38:24Z)
• Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements [62.997667081978825]
  characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in dMRI. We propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model.
  arXiv  Detail & Related papers  (2021-11-15T09:08:27Z)
• MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories [61.3299263929289]
  Simulation-based inference enables learning the parameters of a model even when its likelihood cannot be computed in practice. One class of methods uses data simulated with different parameters to infer an amortized estimator for the likelihood-to-evidence ratio. We show that this approach can be formulated in terms of mutual information between model parameters and simulated data.
  arXiv  Detail & Related papers  (2021-06-03T12:59:16Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)

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.