自由能原理：化繁为简但不过度简化
The free energy principle made simpler but not too simple
AI总结：
本文基于随机动力系统与统计物理，推导自由能原理的特定状态划分、Bayesian 推断和最小作用量变分描述，以解释自组织与主动推断。
摘要：
本文对自由能原理进行了简明阐述，其出发点是用 Langevin 方程表述随机动力系统，最终得到一种可被解读为感知物理学的 Bayesian 力学。文中利用统计物理学的标准结果演练了关键步骤。这些步骤包括：（i）基于由稀疏耦合动力学所继承的条件独立性，建立状态的特定划分；（ii）从 Bayesian 推断的角度阐明该划分的含义；（iii）用最小作用量变分原理描述特定状态的路径。从目的论角度看，自由能原理依据最优 Bayesian 设计与决策制定，为自组织提供了一种规范性说明，即最大化边际似然或 Bayesian 模型证据。总之，从以随机动力系统描述世界出发，我们最终将自组织描述为可被解释为自证成的感知行为；也就是自组装、自创生或主动推断。
英文摘要：
This paper provides a concise description of the free energy principle, starting from a formulation of random dynamical systems in terms of a Langevin equation and ending with a Bayesian mechanics that can be read as a physics of sentience. It rehearses the key steps using standard results from statistical physics. These steps entail (i) establishing a particular partition of states based upon conditional independencies that inherit from sparsely coupled dynamics, (ii) unpacking the implications of this partition in terms of Bayesian inference and (iii) describing the paths of particular states with a variational principle of least action. Teleologically, the free energy principle offers a normative account of self-organisation in terms of optimal Bayesian design and decision-making, in the sense of maximising marginal likelihood or Bayesian model evidence. In summary, starting from a description of the world in terms of random dynamical systems, we end up with a description of self-organisation as sentient behaviour that can be interpreted as self-evidencing; namely, self-assembly, autopoiesis or active inference.
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。
