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% 25/10/2022 10AM

\begin{flashcard}
\begin{theorem*}[Strong Markov Property]
    Let $X$ be $\Markov(\lambda, P)$ and $T$ be a
    stopping time. Conditional \cloze{on $T < \infty$ and
    $X_T = i$, $(X_{T + n})_{n \ge 0}$ is
    $\Markov(\delta_{i}, P)$ and it is independent
    of $X_0, \dots, X_T$.}
\end{theorem*}
\end{flashcard}

\begin{proof}
    Let $x_0, \dots, x_n \in I$, $\omega \in
    \bigcup_k I^k$. Need to show
    \begin{align*}
        \PP(X_T = x_0, \dots, X_{T + n}, (x_0,
        \dots, X_T) = \omega \mid T < \infty, X_T
        = i) \\
        = \delta_{ix_0} P(x_0, x_1) \cdots P(x_{n -
        1}, x_n) \PP((x_0, \dots, X_T) = \omega \mid T
        < \infty, X_T = i)
    \end{align*}
    Let $\omega$ have length $k$. Then
    \begin{align*}
        &\phantom{{}={}} \frac{\PP(X_k = x_0, \dots,
        X_{k + n} = x_n,
        (x_0, \dots, X_k) = \omega, T = k \mid T <
        \infty, X_T = i)}{\PP(T < \infty, X_T =
        i)} \\
        &= \PP(X_k = x_0, \dots, X_{k + n} = x_n
        \mid (X_0, \dots, X_k) = \omega, T = k,
        X_k = i) \\
        &~~~\times \frac{\PP((X_0, \dots,
        X_k) = \omega, T = k, X_k = i)}{\PP(T <
        \infty, X_T = i)} \tag{$*$}
    \end{align*}
    The event $\{T = k\}$ only depends on $X_0,
    \dots, X_k$ ($T$ stopping time). So
    \begin{align*}
        &\phantom{{}={}}
        \PP(X_k = x_0, \dots, X_{k + n} \mid (X_0,
        \dots, X_k) = \omega, T = k, X_k = i) \\
        &=
        \PP(X_k = x_0, \dots, X_{k + n} = x_n \mid X_k =
        i)
    \end{align*}
    by the Markov property. This is also equal to
    \[ \delta_{ix_0} p(x_0, x_1) \cdots p(x_{n -
    1}, x_n) \]
    So the expression in ($*$) is equal to
    \[ \PP((X_0, \dots, X_k) = \omega, T = k \mid
    T < \infty, X_T = i) = \PP((X_0, \dots, X_T) =
    \omega \mid T < \infty, X_T = i) \]
    ($\omega$ has length $k$).
\end{proof}

\subsubsection*{Example}

Consider a Markov chain with $P(0, 1) = 1$, $P(i,
i + 1) = P(i, i - 1) = \half$. (like a random walk
but restricted to $X \ge 0$). Let
\[ T_0 = \inf\{n \ge 0 : X_n = 0\} \]
Let $h_i = \PP_1(T_0 < \infty)$, $h_0 = 1$. What
is $h_1$ equal to?
\[ h_1 = \half + \half h_2 \]
\begin{align*}
    h_2
    &= \PP_2(T_0 < \infty) \\
    &= \PP_2(T_1 < \infty, T_0 < \infty) \\
    &= \PP_2(T_0 < \infty \mid T_1 < \infty) \cdot
    \PP_2(T_1 < \infty) \\
    &= \PP_2(T_0 < \infty \mid T_1 < \infty) h_1
\end{align*}
Conditional on $T_1 < \infty$ ($X_{T_1} = 1$), by
the Markov property $(X_{T_1 + n}){n \ge 0}$ is
$\Markov(\delta_1, P)$. So (under the
conditioning) we can express $T_0 = T_1 +
\tilde{T}_0$, where $\tilde{T}_0$ is
\emph{independent} of $T_1$ and has the same law
as $T_0$ under $\PP_1$.
\begin{align*}
    \PP_2(T_0 < \infty \mid T_1 < \infty)
    &= \PP_2(\tilde{T}_0 + \tilde{T}_1 < \infty
    \mid T_1 < \infty) \\
    &= \PP_1(T_0 < \infty) \\
    &= h_1
\end{align*}
so $h_2 = h_1^2$. So
\[ h_1 = \half + \half h_1^2 \implies h_1 = 1 \]

\subsubsection*{Transience and recurrence}

\begin{definition*}
    A state $i$ is called \emph{recurrent} if
    \[ \PP_i(X_n = i \text{ for infinitely many
    $n$}) = 1 \]
    A state $i$ is called \emph{transient} if
    \[ \PP_i(X_n = i \text{ for infinitely many
    $n$}) = 0 \]
\end{definition*}

\begin{hiddenflashcard}
    \begin{definition*}[Recurrent state]
        A state $i$ is called recurrent if
        \[ \cloze{\PP_i(X_n = i \text{ for
        infinitely many $n$}) = 1} \]
    \end{definition*}
\end{hiddenflashcard}

\begin{hiddenflashcard}
    \begin{definition*}[Transient state]
        A state $i$ is called \emph{transient} if
        \[ \cloze{\PP_i(X_m = i \text{ for infinitely
        many $n$}) = 0} \]
    \end{definition*}
\end{hiddenflashcard}

Let
\[ V_i = \sum_{l = 0}^\infty \mathbbm{1}(X_l =
i) = \text{total number of visits to $i$} \]
We will calculate $\PP_i(V_i > r)$ for some values
of $r$. Let $T_i^(k)$ denote the $k$-th return
time.
\[ \PP_(V_i > 0) = 1 \]
\[ \PP_i(V_i > 1) = \PP_i(T_i^{(1)} < \infty \]
\[ \PP_i(V_i > 2) = \PP_i(T_i^(1) < \infty)^2 \]
More formally: define $T_i^{(0)} = 0$ and for $k
\ge 1$:
\[ T_i^{(k)} = \inf\{n > T_i^{(k - 1)} : X_n = i\} \]
($k$-th return time to $i$). Then
\begin{align*}
    T_i^{(1)} = \inf\{n > 0 : X_n = i\}
\end{align*}
Let $f_i = \PP_i(T_i^{(1)} < \infty)$.

\begin{lemma*}
    For all $r \in \NN$, $\PP_i(V_i > r) = f_i^r$.
    So $V_i$ has a geometric distribution.
\end{lemma*}

\begin{proof}
    True for $r = 0$. Suppose it is true for $r
    \le k$. We will prove it for $k + 1$.
    \begin{align*}
        \PP_i(V_i > k + 1)
        &= \PP_i(T_i^{(k + 1)} < \infty) \\
        &= \PP_i(T_i^{(k + 1)} < \infty,
        T_i^{(k)} < \infty) \\
        &= \PP_i(T_i^{(k + 1)} < \infty \mid
        T_i^{(k)} < \infty) \PP_i(T_i^{(k)} <
        \infty)
    \end{align*}
    The successive return times to $i$ are
    stopping times, so conditional on
    $T_i^{(k)} < \infty$ (and hence $X_{T_i^{(k)}}
    = i$) $(X_{T_i^{(k)} + n})_{n \ge 0}$ is
    $\Markov(\delta_i, P)$ is independent of $X_0,
    \dots, X_{T_i^{(k)}}$. So
    \[ \PP_i(T_i^{(k + 1)} < \infty \mid T_i^{(k)}
    < \infty) = \PP_i(T_i^{(1)} < \infty) = f_i \]
\end{proof}

\begin{theorem*}
    \begin{enumerate}[(a)]
        \item If $f_i = 1$, then $i$ is recurrent and
            \[ \sum_{n \ge 0} p_{ii}(n) = \infty \]
        \item If $f_i < 1$, then $i$ is transient
            and
            \[ \sum_{n \ge 0} p_{ii}(n) < \infty \]
    \end{enumerate}
\end{theorem*}

\begin{proof}
    \[ \EE_i [V_i] = \EE_i \left[ \sum_{l =
    0}^\infty \mathbbm{1} (X_l = i) \right] =
    \sum_{l = 0}^\infty p_{ii}(l) \]
    \begin{enumerate}[(a)]
        \item If $f_i = 1$ then by the lemma,
            $\PP_i(V_i = \infty) = 1$, so $i$ is
            recurrent, so $\EE_i [V_i] = \infty$
            so $\sum_n p_{ii}(n) = \infty$.
        \item If $f_i < 1$ then by the lemma,
            $\EE_i [V_i] = \frac{1}{1 - f_i} <
            \infty$, so $\sum_n p_{ii}(n) <
            \infty$ so $\PP_i(V_i < \infty) = 1$,
            so $i$ is transient.
    \end{enumerate}
\end{proof}

\begin{hiddenflashcard}
    \begin{theorem*}
        Every state is either \emph{recurrent} or
        \emph{transient}.
    \end{theorem*}
    \begin{proof}
        \cloze{
            \begin{itemize}
                \item Let $i$ be a state, and
                    define
                    \[ f_i = \text{probability
                    that we reach $i$ again if we
                    start at $i$} \]
                \item If $f_i = 1$, then
                    \[ \text{probability that we
                    hit $i$ at least $n$
                    times} = 1 \]
                    so recurrent.
                \item If $f_i = 1$, then
                    \[ \text{probability that we
                    hit $i$ at least $n$
                    times} = f_i^n \]
                    summing over all $n$, this is
                    bounded so transient.
            \end{itemize}
        }
    \end{proof}
\end{hiddenflashcard}



\begin{theorem*}
    Let $x$ and $y$ communicate. Then they are
    either both recurrent or both transient.
\end{theorem*}

\begin{proof}
    If $x$ is recurrent, we will show $y$ is also
    recurrent. $x \comm y$ implies that there
    exists $m, r \ge 0$ such that $p_{xy}(m) > 0$,
    $p_{yx}(r) > 0$. Then
    \[ p_{yy}(n + m + r) \ge p_{yx}(r) p_{xx}(n)
    p_{xy}(m) \]
    so
    \[ \sum_{n \ge 0} p_{yy}(n + m + r) \ge
    p_{yx}(r) p_{xy}(m) \sum_{n \ge 0} p_{xx}(n)
    = \infty \]
    so $y$ is also recurrent.
\end{proof}
