Markov blankets, information geometry and stochastic thermodynamics
Markov blankets, information geometry and stochastic thermodynamics
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DOI：
10.1098/rsta.2019.0159
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发表时间：
2020-02-07
期刊：
影响因子：
5
通讯作者：
Friston, Karl
中科院分区：
文献类型：
--
作者：
Parr, Thomas;Da Costa, Lancelot;Friston, Karl
本文考虑了热力学、信息和推理之间的关系。特别是，它探讨了热力学伴随的信念更新，根据变分（自由能）自组织的原则。简而言之，任何（弱混合）随机动力系统，拥有马尔可夫毯，即内部和外部状态的分离，配备了信息几何。这意味着内部状态参数化了外部状态的概率密度。此外，在非平衡稳态下，内部状态的流动可以被解释为在统计学中称为贝叶斯模型证据的量上的梯度流动。简而言之，对于任何拥有马尔可夫毯的系统，都有一个自然的贝叶斯机制。至关重要的是，这意味着有一个明确的内部状态和他们的能量之间的推理执行的联系，其特点是随机热力学。本文是主题问题“协调能源自主计算和智能”的一部分。
This paper considers the relationship between thermodynamics, information and inference. In particular, it explores the thermodynamic concomitants of belief updating, under a variational (free energy) principle for self-organization. In brief, any (weakly mixing) random dynamical system that possesses a Markov blanket-i.e. a separation of internal and external states-is equipped with an information geometry. This means that internal states parametrize a probability density over external states. Furthermore, at non-equilibrium steady-state, the flow of internal states can be construed as a gradient flow on a quantity known in statistics as Bayesian model evidence. In short, there is a natural Bayesian mechanics for any system that possesses a Markov blanket. Crucially, this means that there is an explicit link between the inference performed by internal states and their energetics-as characterized by their stochastic thermodynamics. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.