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Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian

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Summary
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Approximate Bayesian computation (ABC) can yield accurate parameter estimates by accounting for measurement noise. This study presents an efficient algorithm to improve ABC accuracy in systems biology and beyond.

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Area of Science:

  • Computational Biology
  • Statistical Inference

Background:

  • Approximate Bayesian computation (ABC) is widely used for parameter inference in complex stochastic models.
  • Neglecting measurement noise in ABC can lead to inaccurate parameter estimates.
  • ABC can provide exact inference under an implicit measurement noise model.

Purpose of the Study:

  • To address the computational demands of efficiently accounting for measurement noise in ABC.
  • To improve the accuracy of parameter estimates in systems biology and related fields.

Main Methods:

  • Demonstration of erroneous parameter estimates when measurement noise is ignored.
  • Development of an efficient adaptive sequential importance sampling-based algorithm.
  • Application of the algorithm to diverse model types (ODEs, SDEs, Markov jump processes) and noise models (normal, Laplace, Poisson).

Main Results:

  • Illustrative examples show ABC yields erroneous estimates without considering measurement noise.
  • A novel, efficient adaptive algorithm is presented for incorporating measurement noise.
  • The algorithm demonstrates broad applicability across various models and noise types.

Conclusions:

  • The proposed algorithm enhances the accuracy of parameter estimates in ABC analyses.
  • Efficiently accounting for measurement noise is crucial for reliable inference in complex systems.
  • The developed algorithms are available in the open-source pyABC toolbox.