Statistics

Kinory, Shimshon; Freedman, David A.; Pisani, Robert; Purves, R.

doi:10.2307/1268533

Cited by 1 publication

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“…3A). First, we identify the connecting variables using: (i) model variable semantics ( eg , both S V E in the VE model and in the Pa model describe the rate of insulin secretion of a cell); (ii) prior knowledge about the statistical relations between connecting variables ( eg , the plasma glucose concentration G P R in the PR model has been estimated to be double the intracellular glucose concentration G V E in the VE model [51]); (iii) statistical analysis ( eg, S P R in the PR model and in the Pa model present a Pearson correlation coefficient [52] of 0.94 in their corresponding surrogate models) (SI Text 5.2, SI Fig. S2).…”

Section: Resultsmentioning

confidence: 99%

BayesianOccam’s Razorto Optimize Models for Complex Systems

Wang,

Zhao,

Zheng

et al. 2024

Preprint

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Developing and optimizing models for complex systems poses challenges due to the inherent complexity introduced by multiple types of input information and sources of uncertainty. In this study, we utilize Bayesian formalism to analytically examine the propagation of probability in the modeling process and propose quantitative assessments for it. Upon which, we develop a method for optimizing models for complex systems by (i) minimizing model uncertainty; (ii) maximizing model consistency; and (iii) minimizing model complexity, following the BayesianOccam’s razorrationale. We showcase the benefits of this method by optimizing the modeling of the dynamic system of glucose-stimulated insulin secretion in pancreaticβ-cells, leading to an optimized model that demonstrates better alignment with experimental observations compared to the non-optimized one. We anticipate that this method will facilitate the construction of accurate, precise, and sufficiently simple models for diverse complex systems. It is implemented in our open-source softwareIntegrative Modeling Platform(IMP), ensuring its broad applicability.

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