论文标题
网络学习方法来研究世界幸福
Network Learning Approaches to study World Happiness
论文作者
论文摘要
联合国在2011年的决议中宣布追求幸福是一个基本的人类目标，并提出了围绕幸福的公共和经济政策。在本文中，我们使用了两种计算策略，即。预测建模和贝叶斯网络（BNS）自2012年以来已发表的156个国家的处理过的历史幸福指数数据对我们进行了使用。我们使用通用回归神经网络（GRNN）攻击了预测问题，并表明它执行了其他艺术预测模型。为了理解已被证明对世界幸福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方案将连续变量离散为3个级别。低，中和高。然后，通过使用自举通过学习10000个不同的BN来合并信息后，将共识世界的幸福结构固定。最后，在该国立元中使用了通过有条件概率查询的精确推断，以揭示影响幸福的重要特征之间有趣的关系，这对政策制定很有用。
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.
