mlsanityData to 7 Oct 2026

A Comprehensive Guide to Simulation-based Inference in Computational Biology

arXiv 2409.19675 ↗First posted 29 Sep 2024stat.AP q-bio.QM stat.MLcited by 5

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Abstract (arXiv)

Computational models are invaluable in capturing the complexities of real-world biological processes. Yet, the selection of appropriate algorithms for inference tasks, especially when dealing with real-world observational data, remains a challenging and underexplored area. This gap has spurred the development of various parameter estimation algorithms, particularly within the realm of Simulation-Based Inference (SBI), such as neural and statistical SBI methods. Limited research exists on how to make informed choices on SBI methods when faced with real-world data, which often results in some form of model misspecification. In this paper, we provide comprehensive guidelines for deciding between SBI approaches for complex biological models. We apply the guidelines to two agent-based models that describe cellular dynamics using real-world data. Our study unveils a critical insight: while neural SBI methods demand significantly fewer simulations for inference results, they tend to yield biased estimations, a trend persistent even with robust variants of these algorithms. On the other hand, the accuracy of statistical SBI methods enhances substantially as the number of simulations increases. This finding suggests that, given a sufficient computational budget, statistical SBI can surpass neural SBI in performance. Our results not only shed light on the efficacy of different SBI methodologies in real-world scenarios but also suggest potential avenues for enhancing neural SBI approaches. This study is poised to be a useful resource for computational biologists navigating the intricate landscape of SBI in biological modeling.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 34 of its 95 references that are in the map. First posted on arXiv 29 Sep 2024.

Builds on

34 of 95 references.

References · 34

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  2. 2019Neural Spline Flowscited by 590
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  10. 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecificationcited by 48
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  12. 2022Investigating the Impact of Model Misspecification in Neural Simulation-based Inferencecited by 44
22 more
  1. 2022Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrapcited by 41
  2. 2022Truncated proposals for scalable and hassle-free simulation-based inferencecited by 40
  3. 2022GATSBI: Generative Adversarial Training for Simulation-Based Inferencecited by 36
  4. 2019Approximate Bayesian computation with the Wasserstein distancecited by 34
  5. 2023Bayesian Synthetic Likelihoodcited by 29
  6. 2019Variational Bayes under Model Misspecificationcited by 22
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  10. 2022Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networkscited by 11
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2024sbi reloaded: a toolkit for simulation-based inference workflowscited by 23
  2. 2025Simulation-based Bayesian inference under model misspecificationcited by 9
  3. 2025Simulation-based inference for stochastic nonlinear mixed-effects models with applications in systems biologycited by 2
  4. 2026Simulation-Based Inference via Regression Projection and Batched Discrepanciescited by 0
  5. 2026Amortized ratio-estimation importance sampling and localized simulation-based calibration for intractable likelihoodscited by 0