Advancing calibration for stochastic agent-based models in epidemiology with Stein variational inference and Gaussian process surrogates

• URL: http://arxiv.org/abs/2502.19550v1
• Date: Wed, 26 Feb 2025 20:43:32 GMT
• Title: Advancing calibration for stochastic agent-based models in epidemiology with Stein variational inference and Gaussian process surrogates
• Authors: Connor Robertson, Cosmin Safta, Nicholson Collier, Jonathan Ozik, Jaideep Ray,
• Abstract summary: Accurate calibration of agent-based models (ABMs) in epidemiology is crucial to make them useful in public health policy decisions and interventions.<n>Traditional calibration methods, e.g., Markov Chain Monte Carlo (MCMC), that yield a probability density function for the parameters being calibrated, are often computationally expensive.<n>This paper investigates the utility of Stein Variational Inference (SVI) as an alternative calibration technique for epidemiological ABMs approximated by Gaussian process surrogates.
• Score: 1.4447019135112433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate calibration of stochastic agent-based models (ABMs) in epidemiology is crucial to make them useful in public health policy decisions and interventions. Traditional calibration methods, e.g., Markov Chain Monte Carlo (MCMC), that yield a probability density function for the parameters being calibrated, are often computationally expensive. When applied to ABMs which are highly parametrized, the calibration process becomes computationally infeasible. This paper investigates the utility of Stein Variational Inference (SVI) as an alternative calibration technique for stochastic epidemiological ABMs approximated by Gaussian process (GP) surrogates. SVI leverages gradient information to iteratively update a set of particles in the space of parameters being calibrated, offering potential advantages in scalability and efficiency for high-dimensional ABMs. The ensemble of particles yields a joint probability density function for the parameters and serves as the calibration. We compare the performance of SVI and MCMC in calibrating CityCOVID, a stochastic epidemiological ABM, focusing on predictive accuracy and calibration effectiveness. Our results demonstrate that SVI maintains predictive accuracy and calibration effectiveness comparable to MCMC, making it a viable alternative for complex epidemiological models. We also present the practical challenges of using a gradient-based calibration such as SVI which include careful tuning of hyperparameters and monitoring of the particle dynamics.

Related papers

• Epidemiological Model Calibration via Graybox Bayesian Optimization [13.298472586395276]
  Experimental results demonstrate that our proposed graybox variants of BO schemes can efficiently calibrate computationally expensive models.<n>We anticipate that the proposed calibration methods can be extended to enable fast calibration of more complex epidemiological models.
  arXiv  Detail & Related papers  (2024-12-10T05:04:52Z)
• Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models [4.619907534483781]
  computational models that predict drug-target interactions are valuable tools to accelerate the development of new therapeutic agents. However, such models can be poorly calibrated, which results in unreliable uncertainty estimates. We show that combining post hoc calibration method with well-performing uncertainty quantification approaches can boost model accuracy and calibration.
  arXiv  Detail & Related papers  (2024-07-19T10:29:00Z)
• Bayesian calibration of stochastic agent based model via random forest [1.4447019135112433]
  Agent-based models (ABM) provide an excellent framework for modeling outbreaks and interventions in epidemiology. These models are usually and highly parametrized, requiring precise calibration for predictive performance. This paper presents a random forest based surrogate modeling technique to accelerate the evaluation of ABMs.
  arXiv  Detail & Related papers  (2024-06-27T20:50:06Z)
• Online Variational Sequential Monte Carlo [49.97673761305336]
  We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference. Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
  arXiv  Detail & Related papers  (2023-12-19T21:45:38Z)
• Sharp Calibrated Gaussian Processes [58.94710279601622]
  State-of-the-art approaches for designing calibrated models rely on inflating the Gaussian process posterior variance. We present a calibration approach that generates predictive quantiles using a computation inspired by the vanilla Gaussian process posterior variance. Our approach is shown to yield a calibrated model under reasonable assumptions.
  arXiv  Detail & Related papers  (2023-02-23T12:17:36Z)
• Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking [1.5293427903448025]
  Smoothed model checking (smMC) aims at inferring the satisfaction function over the entire parameter space from a limited set of observations. In this paper, we exploit recent advances in probabilistic machine learning to push this limitation forward. We compare the performances of smMC against those of SV-smMC by looking at the scalability, the computational efficiency and the accuracy of the reconstructed satisfaction function.
  arXiv  Detail & Related papers  (2022-05-11T10:43:23Z)
• Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration [57.568461777747515]
  We introduce a novel calibration method, Parametrized Temperature Scaling (PTS) We demonstrate that the performance of accuracy-preserving state-of-the-art post-hoc calibrators is limited by their intrinsic expressive power. We show with extensive experiments that our novel accuracy-preserving approach consistently outperforms existing algorithms across a large number of model architectures, datasets and metrics.
  arXiv  Detail & Related papers  (2021-02-24T10:18:30Z)
• Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso [102.84915019938413]
  Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques. The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge. We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
  arXiv  Detail & Related papers  (2020-09-29T21:17:16Z)
• Unsupervised Calibration under Covariate Shift [92.02278658443166]
  We introduce the problem of calibration under domain shift and propose an importance sampling based approach to address it. We evaluate and discuss the efficacy of our method on both real-world datasets and synthetic datasets.
  arXiv  Detail & Related papers  (2020-06-29T21:50:07Z)
• Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients [54.90670513852325]
  We propose a non-uniform subsampling scheme to improve the sampling accuracy. EWSG is designed so that a non-uniform gradient-MCMC method mimics the statistical behavior of a batch-gradient-MCMC method. In our practical implementation of EWSG, the non-uniform subsampling is performed efficiently via a Metropolis-Hastings chain on the data index.
  arXiv  Detail & Related papers  (2020-02-20T18:56:18Z)

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.