Properties

Properties#

Communication#

  • A state \(i\) leads to state \(j\) denoted as \(i \to j\) if one state can get to another. Formally if \(i\) leads to \(j\) then there exist a time \(t\) where,

    \[ P_{i,j}(t) > 0; \]

  • Two states communicates if \(i \to j \land j \to i\) compactly denoted as \( i \leftrightarrow j\).

  • The chain is irreducible if all \(i,j\) communicates.

Period#

The period \(T\) of a state \(i\) exists if the chain can only start and end at the same state at the interval of \(T\).

  • Slicing out any subset of communicating states in the transition matrix helps you determine its periodicity

  • If \(P_{ii} \neq 0\), then the state \(i\) is aperiodic.

  • All states of irreducible transition matrix must be all share the same period.

Stationarity#

A chain of \(N\) individual states is considered to be in stationarity if for all states \(j\), the transition to state \(j\) is independent of the previous state \(i\).

\[ P_{ij}(n) = \text{const}, \quad \text{for fixed $i$ and all $j$} \]

We express this formally as a state has stationarity as the number of steps \(n \to \infty\)

\[\begin{split} \begin{gather*} P_{i,j}(n) \rightarrow \pi(j), \quad \text{as }n \to \infty\\ \sum_{j \in S} \pi(j) = 1 \end{gather*} \end{split}\]

Stationarity of Irreducible Aperiodioc Markov Chain : All irreducible and aperiodic Markov chain converges to stationarity

Stationarity States are Positive : $\( \pi(j) > 0 \)$

Balance Equation : Satisfies the balance equation for \(\vec \pi\) the probability row vector for all states \(j\):

$$
\pi(j) = \sum_{i \in S} P(i,j)\pi(i)
$$

$$
\vec \pi P = \vec \pi
$$

Any constant factor of the RHS also satisfies the balance equation.

Steady/Stationary State Distribution : If \(X_n\) is distributed as \(\pi(j)\) then \(X_m\), for \(m > n\), is also distributed as \(\pi(j)\).

$$
\begin{gather*}
P_{ij}(n+1) = P_{ij}(n) = \pi(j)\\
\big\Downarrow\\
P_{ij}(m) = \pi(j); \quad m > n
\end{gather*}
$$

Long Run Expected Proportion of Time in State : Let \(I_t(j)\) be the indicator of the event \(\set{X_m = j}\), for some time until \(t_f\), the total proportion of time spent on state \(j\) is given by,

$$
\frac{1}{t_f} \sum_{t=1}^{t_f} I_m(j)
$$

The expected proportion of time the chain spent on $j$ is then,

$$
\mathbb E\left[\frac{1}{t_f} \sum_{t=1}^{t_f} I_m(j) \right] = \frac{1}{t_f}\sum_{t=1}^{t_f} P_{ij}(t)
$$

As $t_f = n \to \infty$,

$$
\frac{1}{n}\sum_{t=1}^n P_{ij}(t) = \pi(j)
$$

Thus the expected value of the proportion of time spent on state $j$ in the long run is given by the steady state proportion $\pi(j)$.
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