论文标题
自由能最小化：建模，推理，学习和优化的统一框架
Free Energy Minimization: A Unified Framework for Modelling, Inference, Learning,and Optimization
论文作者
论文摘要
这些讲义的目的是回顾自由能最小化的问题，作为最大熵建模，广义贝叶斯推断的定义的统一框架，具有潜在变量的学习，对概括的统计学习分析和局部优化。自由能最小化首先是在这里和历史上作为热力学原理引入的。然后，在Fenchel二元性的背景下进行数学描述。最后，从基本原理开始涵盖了建模，推理，学习和优化的应用程序。
The goal of these lecture notes is to review the problem of free energy minimization as a unified framework underlying the definition of maximum entropy modelling, generalized Bayesian inference, learning with latent variables, statistical learning analysis of generalization,and local optimization. Free energy minimization is first introduced, here and historically, as a thermodynamic principle. Then, it is described mathematically in the context of Fenchel duality. Finally, the mentioned applications to modelling, inference, learning, and optimization are covered starting from basic principles.
