网络学习方法研究世界幸福
Network Learning Approaches to study World Happiness
摘要：
联合国在2011年的决议中宣布追求幸福是人类的基本目标，并提出了以幸福为中心的公共和经济政策。本文使用了两种计算策略，即预测建模和贝叶斯网络（BN），对联合国自2012年以来发布的156个国家的历史幸福指数数据进行了处理。我们使用广义回归神经网络（GRNNs）解决了预测问题，并表明它优于其他最先进的预测模型。为了了解已被证明对世界幸福感有重大影响的关键特征之间的因果关系，我们首先使用手动离散化方案将连续变量离散为3个级别，即低，中，高。一个共识的世界幸福BN结构，然后固定后，通过学习10000个不同的BN使用自举合并信息。最后，通过条件概率查询的精确推理，这个BN解开有趣的关系，影响幸福的重要功能，这将是有用的政策制定。
英文摘要：
The United Nations in its 2011 resolution declared the pursuit of happiness a fundamental human goal and proposed public and economic policies centered around happiness. In this paper we used 2 types of computational strategies viz. Predictive Modelling and Bayesian Networks (BNs) to model the processed historical happiness index data of 156 nations published by UN since 2012. We attacked the problem of prediction using General Regression Neural Networks (GRNNs) and show that it out performs other state of the art predictive models. To understand causal links amongst key features that have been proven to have a significant impact on world happiness, we first used a manual discretization scheme to discretize continuous variables into 3 levels viz. Low, Medium and High. A consensus World Happiness BN structure was then fixed after amalgamating information by learning 10000 different BNs using bootstrapping. Lastly, exact inference through conditional probability queries was used on this BN to unravel interesting relationships among the important features affecting happiness which would be useful in policy making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
