Papers

A Comprehensive Guide to Simulation-based Inference in Computational Biology

X Wang, RP Kelly, AL Jenner, DJ Warne… - arXiv preprint arXiv …, 2024 - arxiv.org
Statistics paper stat.AP Suggest

… 2.2 Simulation-based Inference We define Simulation-Based Inference (SBI) as an inference technique that does not explicitly evaluate the likelihood function, but rather …

Cited by Link to paper

BibTeX

@article{2409.19675v1,
Author        = {Xiaoyu Wang and Ryan P. Kelly and Adrianne L. Jenner and David J. Warne and Christopher Drovandi},
Title         = {A Comprehensive Guide to Simulation-based Inference in Computational
  Biology},
Eprint        = {2409.19675v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.AP},
Abstract      = {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.},
Year          = {2024},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2409.19675v1},
File          = {2409.19675v1.pdf}
}

Share