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英文文献：Inference for Nonparametric Productivity Networks: A Pseudo-likelihood Approach-非参数生产力网络的推论:一种伪似然方法
英文文献作者：Moriah B. Bostian,Cinzia Daraio,Rolf Fare,Shawna Grosskopf,Maria Grazia Izzo,Luca Leuzzi,Giancarlo Ruocco,William L. Weber
英文文献摘要：
Networks are general models that represent the relationships within or between systems widely studied in statistical mechanics. Nonparametric productivity networks (Network-DEA) typically analyzes the networks in a descriptive rather than statistical framework. We fill this gap by developing a general framework-involving information science, machine learning and statistical inference from the physics of complex systems- for modeling the production process based on the axiomatics of Network-DEA connected to Georgescu-Roegen funds and flows model. The proposed statistical approach allows us to infer the network topology in a Bayesian framework. An application to assess knowledge productivity at a world-country level is provided.
网络是代表统计力学中广泛研究的系统内部或系统之间关系的一般模型。非参数生产力网络(网络- dea)通常以描述性而非统计框架分析网络。我们通过开发一个通用框架(包括信息科学、机器学习和从复杂系统的物理中得出的统计推断)来填补这一空白，该框架基于与georgescul - roegen资金和流模型相连接的网络- dea公理来建模生产过程。提出的统计方法允许我们推断网络拓扑在贝叶斯框架。提供了一个在世界国家水平上评估知识生产力的应用程序。
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