朴素贝叶斯图弹性网：推动差异网络分析的进展
A naıve Bayesian graphical elastic net: driving advances in differential network analysis
AI总结：
本研究提出朴素贝叶斯自适应图弹性网（BAE）先验用于估计差异网络，推导了启发式结构确定机制和块Gibbs采样器，在合成与真实数据集上验证了其优越的适应性和实用性。
摘要：
差异网络（DNs）是封装复杂系统内相互作用的工具，本研究将其置于贝叶斯视角下。引入了一种新颖的朴素贝叶斯自适应图弹性网（BAE）先验来估计DN的组件。推导了启发式结构确定机制和块Gibbs采样器。最初在包含各种网络拓扑的合成数据集上评估性能，旨在评估并与贝叶斯自适应图套索和岭型过程的灵活性进行比较。朴素BAE估计器始终位列前两名表现者，突显了其固有的适应性。最后，将BAE应用于肿瘤学、肾脏病学和酿酒学等不同领域的真实世界数据集，强调了其在全面网络分析中的潜在实用性。
英文摘要：
Differential Networks (DNs), tools that encapsulate interactions within intricate systems, are brought under the Bayesian lens in this research. A novel naıve Bayesian adaptive graphical elastic net (BAE) prior is introduced to estimate the components of the DN. A heuristic structure determination mechanism and a block Gibbs sampler are derived. Performance is initially gauged on synthetic datasets encompassing various network topologies, aiming to assess and compare the flexibility to those of the Bayesian adaptive graphical lasso and ridge-type procedures. The naıve BAE estimator consistently ranks within the top two performers, highlighting its inherent adaptability. Finally, the BAE is applied to real-world datasets across diverse domains such as oncology, nephrology, and enology, underscoring its potential utility in comprehensive network analysis.
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