Consider the modeling of a communication system. A message is sent through this system, arriving at a state of the system at each time. The message is thus the temporal reconfiguration chain of the system as it takes on different states from its possibility set \(\Omega\). Due to noise in the message-propagation channel, i.e. the necessity of interpretation within any deciphering of *the meaning* of the message from its natural ambiguity, we can only know the probability of the system’s state at a certain time, in that the full interpretation set of the message is a sequence of probability distributions as the probability of it having a certain state at a certain time. Performing this operation discretely in finite time, we can only sample the code as a sequence of states a number of trial-repetitions (\(N\)), and take the frequency of each state at each time to be its approximate probability. We consider this to be empirical interpretation of the message. With \(b\) possible states to our system, let \(\Sigma_b\) be the symbolic code-space of all possible message sequences, including bi-directionally infinite ones, i.e. where the starting and finishing time is not known. Thus, a given empirically sampled message sequence is given by \(\tilde{x^l}=(x^l_k)\{k=-\infty\}^{k=\infty}, x^l_k \in \{0, \cdots, b-1\}\),* *where the empirical interpretation is given by the frequency distributions* *\(\tilde{f}_{x(i)}(k)=\frac{1}{N}\sum_{l=0}^N \bf{1}_{x^l{k}=i}\).

Stationary Distribution: What are the initial distribution conditions such that the distribution positions do not change over time? Let \(f\) be the time iterational operation of the system on its space \(X\). While the times are counted \(t\in \mathbb{N}\), the length of each time step \(t_i \ \rightarrow \ t_{i+1}\) is given by \(\Delta t_i\), such that the real time \(T\) is given by \(T(t_i)=\sum_{k=0}^{i-1} \Delta t_k\). The system at a given time is given by the probability distributions of the different states, which are the macro partitions \(\omega \in \Omega\) of the micro states \(x \in \omega\) as thus \(F_t(\omega_k)=f^t(x \in \omega)=\mathbb{P}(X_t=k)\). \(F_t(\omega_i)=F(t)[i]\) is the cumulative time distribution of the class-partitioned state-space distributions. The stationary distribution is such that \(F(0)=F(t)\).

Consider the string of numbers \((x_k)_{k=T_0}^{T_N}\) from N iterations of an experiment. What does it mean for the underlying numbers to be normally distributed? It means that the experiment is independent of time. The distribution stays the same at each time interval. Given a time-dependent process, the averages of these empirical measurement numbers will always be normal. Thus, normality is the stationary distribution of the averaging process. For random time-lengths, it averages all the values in that time-interval, without remembering the length of time or, equivalently, the number of values. markov – time homogeneity. Consider a system that changes states over time between \(b\) different state-indexes \(\{0, \cdots, b-1\}\). When the system-state appears as 0, we perform an average of the previous values between its present time and the previous 0 occurrence. Thus, the variable of 0 although an intrinsic part of the object of measurement is in fact a property of the subject performing the measurement, as when he or she decides to stop the measurement process and perform an averaging of the results. We call such a variable the *mimetic* basis when its objectivity depends upon a subjectivity in the action of measurement and the *mimetic* dynamics are given by the relationship between the occurrence of a 0 and the other states. Here 0 is the stopping time, where a string of results are thus averaged before continuing. Let \(T_0^{(k)}=\inf\{m: m_{i=1}^{\tau_0^{k}}\hat{f}(X_{T_0}^{(k-1)}+i)\}\), where \(\hat{f}(i)\) gives the actual measured value from the *i*th state of the system. In reality, the system’s function time-inducing function \(f\) has resulted in a particular value \(f(x) \in X\) before it was partitioned into \(\Omega\) via \(P\), although here the time-inverting (dys)function \(\hat{f}\) determines this original pre-value from the result. Often the empirical \(\tilde{f}\) is used from the average of the state’s values, i.e. \(\tilde{f}x^{k}(i)=\frac{1}{M}\sum_{k=0}^{M} f^{n_k}(x)\) such that \(x, f^{n_k}(x)\in \omega_i, n_{k-1}<l<n_{k}, f^l(x) \notin \omega_i, M=N_{T_0^{(k)}}(i)\) and \(N_{n}(i)=|\{m: X_m=i, m\leq n \}| \), which thus takes the average value from an M-length self-communication string for a particular state. \( \{\tilde{S}_k(x)= \frac{1}{\tau_0^{k}-1}\sum_{i=1}^{\tau_0^{k}}\tilde{f}x^{k}(X_{T_0^{(k-1)}+i})\}_{k=1}^{N}\) & \(\{S_k(x)= \frac{1}{\tau_0^{k}-1}\sum_{i=1}^{\tau_0^{k}} f^{T_0^{(k-1)}+i}(x) \}_{k=1}^{N}\) approach normal distributions as \(N\) increases if state-0 is independent of the others.