<scp>B</scp>ayesian Model Selection

George, Edward I.

doi:10.1002/9781118445112.stat00228

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…Direct computation of Eq. ( 8) can become intractable because the integral is often over a high-dimension space and thus requires approximations by Monte Carlo integration, Bridge Sampling, or sequential Monte Carlo [19], [61]- [63].…”

Section: Bayesian Model Averagingmentioning

confidence: 99%

Increasing certainty in systems biology models using Bayesian multimodel inference

Linden-Santangeli,

Zhang,

Kramer

et al. 2024

Preprint

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Mathematical models are indispensable to the system biology toolkit for studying the structure and behavior of intracellular signaling networks. A common approach to modeling is to develop a system of equations that encode the known biology using approximations and simplifying assumptions. As a result, the same signaling pathway can be represented by multiple models, each with its set of underlying assumptions, which opens up challenges for model selection and decreases certainty in model predictions. Here, we use Bayesian multimodel inference to develop a framework to increase certainty in systems biology models. Using models of the extracellular regulated kinase (ERK) pathway, we first show that multimodel inference increases predictive certainty and yields predictors that are robust to changes in the set of available models. We then show that predictions made with multimodel inference are robust to data uncertainties introduced by decreasing the measurement duration and reducing the sample size. Finally, we use multimodel inference to identify a new model to explain experimentally measured sub-cellular location-specific ERK activity dynamics. In summary, our framework highlights multimodel inference as a disciplined approach to increasing the certainty of intracellular signaling activity predictions.

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Section: Bayesian Model Averagingmentioning

confidence: 99%

Increasing certainty in systems biology models using Bayesian multimodel inference

Linden-Santangeli,

Zhang,

Kramer

et al. 2024

Preprint

View full text Add to dashboard Cite

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Stochastic Search Variable Selection (SSVS)

Perrakis

Ntzoufras

2015

Wiley StatsRef: Statistics Reference Online

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The stochastic search variable selection (SSVS), introduced by George and McCulloch [1] , is one of the prominent Bayesian variable selection approaches for regression problems. Some of the basic principles of modern Bayesian variable selection methods were first introduced via the SSVS algorithm such as the use of a vector of variable inclusion indicators. SSVS can effectively search large model spaces, identifying the maximum a posteriori and median probability models, and also readily produce Bayesian model averaging estimates. A number of generalizations and extensions of the method have appeared in the statistical literature implementing SSVS to a variety of applications such as generalized linear models, contingency tables, time series data, and factor analysis.

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Bayesian Model Selection

Cited by 2 publications

References 40 publications

Increasing certainty in systems biology models using Bayesian multimodel inference

Increasing certainty in systems biology models using Bayesian multimodel inference

Stochastic Search Variable Selection (SSVS)

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