{
  "abstract": "Recovering the interaction structure of complex stochastic dynamical systems from sparse time-series observations is an inherently ill-posed inverse problem. The posterior over network configurations is high-dimensional, highly degenerate, and shaped by the non-equilibrium nature of the available data. To address this, we propose a fully Bayesian, ensemble-based inference framework. Our approach grounds a continuous-time Stochastic Differential Equation (SDE) likelihood formulation---acting mathematically as an Onsager-Machlup quasi-potential for non-equilibrium paths---within a non-parametric Minimum Description Length (MDL) structural prior. This framework rigorously penalizes model complexity and avoids kinetic overfitting by forcing continuous parameters into a finite vocabulary of discrete categories. The joint posterior is optimized using a block-coordinate scheme combining sub-quadratic Markov Chain Monte Carlo (MCMC) topological sampling with deterministic Nelder-Mead parameter optimization. Using highly non-linear macroscopic population dynamics as a benchmark, we demonstrate that inference requires non-stationary transient data to resolve causal information. Furthermore, we prove that propagating structural uncertainty via ensemble-based posterior analysis produces significantly more robust and reliable predictions for downstream perturbation outcomes than traditional single-point (MAP) estimates. The framework is highly generalizable, capturing fundamental topological constraints even under severe generative model mismatches, offering a robust tool for uncertainty-aware inference in complex stochastic systems.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.19744",
  "author_total": 3,
  "authors": [
    {
      "name": "Francesco Zambelli"
    },
    {
      "name": "Valeria D'Andrea"
    },
    {
      "name": "Manlio De Domenico"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/db628rc20yjar.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.19744",
  "fulltext_url": null,
  "id": "db628rc20yjar",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.19744",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.dis-nn"
    ],
    [
      "openalex",
      "2613|Markov Chains and Monte Carlo Methods"
    ]
  ],
  "openalex": "W7222288631",
  "pdf_url": "https://arxiv.org/pdf/2610.19744",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/db628rc20yjar.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:31Z",
      "native_id": "2610.19744",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.19744"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.dis-nn",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Resolving Structural Uncertainty in Stochastic Dynamical Systems via Ensemble-Based Bayesian Inference",
  "title_text": "Resolving Structural Uncertainty in Stochastic Dynamical Systems via Ensemble-Based Bayesian Inference",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:31Z",
  "url": "https://research.draconitic.com/paper/db628rc20yjar",
  "venue": "arXiv",
  "versions": []
}