Bayesian Model Selection With Network Based Diffusion Analysis

Frontiers in Psychology

William John Edward Hoppitt¹  Andrew eWhalen² 

[1] University of Leeds;University of St Andrews;

关键词: Social learning;    bayesian model selection;    statistical methods;    WAIC;    Network Based Diffusion Analysis;   

DOI  :  10.3389/fpsyg.2016.00409

来源: DOAJ

【 摘 要 】

A number of recent studies have used Network Based Diffusion Analysis (NBDA) to detect the role of social transmission in the spread of a novel behavior through a population. In this paper we present a unified framework for performing NBDA in a Bayesian setting, and demonstrate how the Watanabe Akaike Information Criteria (WAIC) can be used for model selection. We present a specific example of applying this method to Time to Acquisition Diffusion Analysis (TADA). To examine the robustness of this technique, we performed a large scale simulation study and found that NBDA using WAIC could recover the correct model of social transmission under a wide range of cases, including under the presence of random effects, individual level variables, and alternative models of social transmission. This work suggests that NBDA is an effective and widely applicable tool for uncovering whether social transmission underpins the spread of a novel behavior, and may still provide accurate results even when key model assumptions are relaxed.

【 授权许可】

Unknown