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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Bayesian Model Selection with Network Based Diffusion Analysis.

Andrew Whalen1, William J E Hoppitt2

  • 1School of Biology, University of St. Andrews St. Andrews, UK.

Frontiers in Psychology
|April 20, 2016
PubMed
Summary
This summary is machine-generated.

Network Based Diffusion Analysis (NBDA) provides a robust Bayesian framework for understanding social transmission of novel behaviors. This method, utilizing Watanabe Akaike Information Criteria (WAIC), accurately identifies transmission models even with complex data variations.

Keywords:
Bayesian model selectionNetwork Based Diffusion AnalysisWAICsocial learningstatistical methods

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Area of Science:

  • Social Sciences
  • Computational Biology
  • Statistical Modeling

Background:

  • Network Based Diffusion Analysis (NBDA) is increasingly used to study social transmission of behaviors.
  • Existing methods may lack a unified framework or robust model selection criteria.

Purpose of the Study:

  • To present a unified Bayesian framework for NBDA.
  • To demonstrate the utility of Watanabe Akaike Information Criteria (WAIC) for model selection within NBDA.
  • To assess the robustness and applicability of this framework.

Main Methods:

  • Developed a Bayesian framework for NBDA.
  • Integrated WAIC for model selection.
  • Conducted large-scale simulations to test the method's performance.
  • Applied the framework to Time to Acquisition Diffusion Analysis (TADA).

Main Results:

  • The Bayesian NBDA framework with WAIC successfully recovered correct social transmission models in simulations.
  • The method demonstrated robustness across various conditions, including random effects and alternative models.
  • Accurate results were obtained even when key model assumptions were relaxed.

Conclusions:

  • Bayesian NBDA using WAIC is an effective and versatile tool for detecting social transmission.
  • This framework provides reliable insights into behavior spread, even with complex or imperfect data.
  • The approach enhances our ability to understand the dynamics of novel behavior adoption.