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The existence of the typical set is key for the consistence of the ensemble formalization of statistical mechanics. We demonstrate here that the typical set can be defined and characterized for a general class of stochastic processes. This includes processes showing arbitrary path dependence, long range correlations or dynamic sampling spaces. We show how the typical set is characterized from general forms of entropy and how one can transform these general entropic forms into extensive functionals and, in some cases, to Shannon path entropy. The definition of the typical set and generalized forms of entropy for systems with arbitrary phase space growth may help to provide an ensemble picture for the thermodynamic paths of many systems away from equilibrium. In particular, we argue that a theory of expanding/shrinking phase spaces in processes displaying an intrinsic degree of stochasticity may lead to new frameworks for exploring the emergence of complexity and robust properties in open ended evolutionary systems and, in particular, of biological systems.
The stochastic entropy generated during the evolution of a system interacting with an environment may be separated into three components, but only two of these have a non-negative mean. The third component of entropy production is associated with the
The total entropy production and its three constituent components are described both as fluctuating trajectory-dependent quantities and as averaged contributions in the context of the continuous Markovian dynamics, described by stochastic differentia
The quantum entropy-typical subspace theory is specified. It is shown that any mixed state with von Neumann entropy less than h can be preserved approximately by the entropy-typical subspace with entropy= h. This result implies an universal compressi
Computing the stochastic entropy production associated with the evolution of a stochastic dynamical system is a well-established problem. In a small number of cases such as the Ornstein-Uhlenbeck process, of which we give a complete exposition, the d
A change in a stochastic system has three representations: Probabilistic, statistical, and informational: (i) is based on random variable $u(omega)totilde{u}(omega)$; this induces (ii) the probability distributions $F_u(x)to F_{tilde{u}}(x)$, $xinmat