  This paper develops a Bayesian mechanics for adaptive systems.
  Firstly, we model the interface between a system and its environment with a
Markov blanket. This affords conditions under which states internal to the
blanket encode information about external states.
  Second, we introduce dynamics and represent adaptive systems as Markov
blankets at steady-state. This allows us to identify a wide class of systems
whose internal states appear to infer external states, consistent with
variational inference in Bayesian statistics and theoretical neuroscience.
  Finally, we partition the blanket into sensory and active states. It follows
that active states can be seen as performing active inference and well-known
forms of stochastic control (such as PID control), which are prominent
formulations of adaptive behaviour in theoretical biology and engineering.
