lecture 22
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We want to start with the basics of the theory of Markov chains.
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We want to start with the basics of the theory of Markov chains.
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\end{goal}
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\end{goal}
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% \begin{example}[Markov chains with two states]
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\begin{example}[Markov chains with two states]
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% Suppose there are two states of a phone line,
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Suppose there are two states of a phone line,
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% $0$,``free'', or $1$, ``busy''.
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$0$,``free'', or $1$, ``busy''.
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% We assume that the state only changes at discrete units of time
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We assume that the state only changes at discrete units of time
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% and model this as a sequence of random variables.
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and model this as a sequence of random variables.
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% Assume
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Assume
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% \begin{IEEEeqnarra*}{rCl}
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\begin{IEEEeqnarray*}{rCl}
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% \bP[X_{n+1} = 0 | X_n = 0] &=& p\\
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\bP[X_{n+1} = 0 | X_n = 0] &=& p\\
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% \bP[X_{n+1} = 0 | X_n = 1] &=& (1-p)\\
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\bP[X_{n+1} = 0 | X_n = 1] &=& (1-p)\\
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% \bP[X_{n+1} = 1 | X_n = 0] &=& q\\
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\bP[X_{n+1} = 1 | X_n = 0] &=& q\\
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% \bP[X_{n+1} = 1 | X_n = 1] &=& (1-q)
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\bP[X_{n+1} = 1 | X_n = 1] &=& (1-q)
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% \end{IEEEeqnarra*}
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\end{IEEEeqnarray*}
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% for some $p,q \in (0,1)$.
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for some $p,q \in (0,1)$.
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% We can write this as a matrix
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We can write this as a matrix
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% \begin{IEEEeqnarra*}{rCl}
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\begin{IEEEeqnarray*}{rCl}
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% P &=& \begin{pmatrix}
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P &=& \begin{pmatrix}
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% p & (1-p) \\
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p & (1-p) \\
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% q & (1-q)
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q & (1-q)
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% \end{pmatrix}
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\end{pmatrix}
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% \end{IEEEeqnarra*}
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\end{IEEEeqnarray*}
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% Note that the rows of this matrix sum up to $1$.
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Note that the rows of this matrix sum up to $1$.
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%
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% Additionally, we make the following assmption:
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Additionally, we make the following assmption:
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% Given that at some time $n$, the phone is in state $i \in \{0,1\}$,
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Given that at some time $n$, the phone is in state $i \in \{0,1\}$,
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% the behavior of the phone after time $n$ does not depend
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the behavior of the phone after time $n$ does not depend
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% on the way, the phone reached state $i$.
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on the way, the phone reached state $i$.
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%
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% \begin{question}
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\begin{question}
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% Suppose $X_0 = 0$.
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Suppose $X_0 = 0$.
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% What is the probability, that the phone will be free at times
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What is the probability, that the phone will be free at times
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% $1 \& 2$ and will become busy at time $3$,
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$1 \& 2$ and will become busy at time $3$,
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% i.e.~what is $\bP[X_1 = 0, X_2 = 0, X_3 = 1]$?
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i.e.~what is $\bP[X_1 = 0, X_2 = 0, X_3 = 1]$?
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% \end{question}
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\end{question}
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% We have
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We have
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% \begin{IEEEeqnarra*}{rCl}
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\begin{IEEEeqnarray*}{rCl}
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% \bP[X_1 = 0, X_2 = 0, X_3 = 1]
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\bP[X_1 = 0, X_2 = 0, X_3 = 1]
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% &=& \bP[X_3 = 0 | X_2 = 0, X_1 = 0] \bP[X_2 = 0, X_1 = 0]\\
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&=& \bP[X_3 = 0 | X_2 = 0, X_1 = 0] \bP[X_2 = 0, X_1 = 0]\\
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% &=& \bP[X_3 = 0 | X_2 = 0] \bP[X_2 = 0, X_1 = 0]\\
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&=& \bP[X_3 = 0 | X_2 = 0] \bP[X_2 = 0, X_1 = 0]\\
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% &=& \bP[X_3 = 0 | X_2 = 0] \bP[X_2 = 0 | X_1 = 0] \bP[X_1 = 0]\\
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&=& \bP[X_3 = 0 | X_2 = 0] \bP[X_2 = 0 | X_1 = 0] \bP[X_1 = 0]\\
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% &=& P_{0,1} P_{0,0} P_{0,0}
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&=& P_{0,1} P_{0,0} P_{0,0}
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% \end{IEEEeqnarra*}
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\end{IEEEeqnarray*}
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%
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% \begin{question}
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\begin{question}
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% Assume $X_0 = 0$. What is $\bP[X_3 = 1]$?
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Assume $X_0 = 0$. What is $\bP[X_3 = 1]$?
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% \end{question}
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\end{question}
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% For $\{X_3 = 1\}$ to happen, we need to look at the following
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For $\{X_3 = 1\}$ to happen, we need to look at the following
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% disjoint events:
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disjoint events:
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% % \begin{IEEEeqnarra*}{rCl}
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\begin{IEEEeqnarray*}{rCl}
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% % \bP(\{X_3 = 1, X_2 = 0, X_1 = 0\}) &=& P_{0,1} P_{0,0}^2,\\
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\bP(\{X_3 = 1, X_2 = 0, X_1 = 0\}) &=& P_{0,1} P_{0,0}^2,\\
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% % \bP(\{X_3 = 1, X_2 = 0, X_1 = 1\}) &=& P_{0,1}^2 P_{1,0},\\
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\bP(\{X_3 = 1, X_2 = 0, X_1 = 1\}) &=& P_{0,1}^2 P_{1,0},\\
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% % \bP(\{X_3 = 1, X_2 = 1, X_1 = 0\}) &=& P_{0,0} P_{0,1} P_{1,1},\\
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\bP(\{X_3 = 1, X_2 = 1, X_1 = 0\}) &=& P_{0,0} P_{0,1} P_{1,1},\\
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% % \bP(\{X_3 = 1, X_2 = 1, X_1 = 1\}) &=& P_{0,1} P_{1,1}^2.
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\bP(\{X_3 = 1, X_2 = 1, X_1 = 1\}) &=& P_{0,1} P_{1,1}^2.
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% % \end{IEEEeqnarr*}
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\end{IEEEeqnarray*}
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%
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% More generally, consider a Matrix $P \in (0,1)^{n \times n}$
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More generally, consider a Matrix $P \in (0,1)^{n \times n}$
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% whose rows sum up to $1$.
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whose rows sum up to $1$.
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% Then we get a Markov Chain with $n$ states
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Then we get a Markov Chain with $n$ states
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% by defining $\bP[X_{n+1} = i | X_{n} = j] = P_{i,j}$.
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by defining $\bP[X_{n+1} = i | X_{n} = j] = P_{i,j}$.
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%
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\end{example}
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% \end{example}
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\begin{definition}
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\begin{definition}
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Let $E$ denote a \vocab{discrete state space},
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Let $E$ denote a \vocab{discrete state space},
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@ -117,15 +116,15 @@
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Consider
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Consider
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\begin{IEEEeqnarray*}{rCl}
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\begin{IEEEeqnarray*}{rCl}
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P &=&
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P &=&
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\begin{pmatrix}
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\begin{pmatrix}
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& \ddots & \ddots & \ddots & & & & & 0\\
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& \ddots & \ddots & \ddots & & & & & 0\\
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\ldots & 0 & \frac{1}{2} & 0 & \frac{1}{2} & 0 & \ldots \\
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\ldots & 0 & \frac{1}{2} & 0 & \frac{1}{2} & 0 & \ldots \\
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& \ldots & 0 & \frac{1}{2} & 0 & \frac{1}{2} & 0 & \ldots \\
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& \ldots & 0 & \frac{1}{2} & 0 & \frac{1}{2} & 0 & \ldots \\
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& & \ldots & 0 & \frac{1}{2} & 0 & \frac{1}{2} & 0 & \ldots \\
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& & \ldots & 0 & \frac{1}{2} & 0 & \frac{1}{2} & 0 & \ldots \\
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0 & & & & & \ddots & \ddots & \ddots & \\
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0 & & & & & \ddots & \ddots & \ddots & \\
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\end{pmatrix}
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\end{pmatrix}
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\end{IEEEeqnarray*}
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\end{IEEEeqnarray*}
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\end{example}
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\end{example}
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% \begin{example}
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% \begin{example}
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For every $x \in E$,
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For every $x \in E$,
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let $\mathbf{P}(x, \cdot )$ be a probability measure on $E$.%
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let $\mathbf{P}(x, \cdot )$ be a probability measure on $E$.%
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\footnote{$\mathbf{P}(x,\cdot )$ corresponds to a row of our matrix in the discrete case}
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\footnote{$\mathbf{P}(x,\cdot )$ corresponds to a row of our matrix in the discrete case}
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Given the triples $(E, \alpha, \{\mathbf{P}(x, \cdot )\}_{x \in E})$,
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Given the triples $(E, \alpha, \{\mathbf{P}(x, \cdot )\}_{x \in E})$,
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we say that a stochastic process $(X_n)_{n \ge 0}$
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we say that a stochastic process $(X_n)_{n \ge 0}$
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is a \vocab[Markov chain]{Markov chain taking values on $E$ %
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is a \vocab[Markov chain]{Markov chain taking values on $E$ %
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with starting distribution $\alpha$ %
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with starting distribution $\alpha$ %
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and transition probability $\{\mathbf{P}(x, \cdot )\}_{x \in E}$}
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and transition probability $\{\mathbf{P}(x, \cdot )\}_{x \in E}$}
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\[
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\[
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\bE[f(X_{n+1}) | \cF_n] = \bE[f(X_{n+1}) | X_n]
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\bE[f(X_{n+1}) | \cF_n] = \bE[f(X_{n+1}) | X_n]
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= \int_E f(y) \mathbf{P}(X_n, \dif y) \text{ a.s.}
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= \int_E f(y) \mathbf{P}(X_n, \dif y) \text{ a.s.}
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\]
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\]
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\end{enumerate}
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\end{enumerate}
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\end{definition}
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\end{definition}
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\begin{remark}
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\begin{remark}
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This agrees with the definition in the discrete case,
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This agrees with the definition in the discrete case,
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\begin{notation}
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\begin{notation}
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If $\{\mathbf{P}(x, \cdot )\}_{x \in E}$ is a transition probability,
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If $\{\mathbf{P}(x, \cdot )\}_{x \in E}$ is a transition probability,
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then for all $f: E \to \R$ bounded and measurable,
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then for all $f: E \to \R$ bounded and measurable,
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define $\mathbf{P} : \cB_{\text{bdd}}(E) \to \cB_{\text{bdd}}$
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define $\mathbf{P} : \cB_{\text{bdd}}(E) \to \cB_{\text{bdd}}$
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by
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by
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\[
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\[
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(\mathbf{P} f)(x) \coloneqq \int_E f(y) \mathbf{P}(x, \dif y).
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(\mathbf{P} f)(x) \coloneqq \int_E f(y) \mathbf{P}(x, \dif y).
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\]
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\]
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\end{notation}
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\end{notation}
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We get the following fundamental link between martingales and Markov chains:
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We get the following fundamental link between martingales and Markov chains:
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\begin{theorem}
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\begin{theorem}
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\label{martingalesandmarkovchains}
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\label{martingalesandmarkovchains}
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Suppose $(E, \alpha, \{\mathbf{P}(x, \cdot )\}_{x \in E})$
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Suppose $(E, \alpha, \{\mathbf{P}(x, \cdot )\}_{x \in E})$
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is given.
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is given.
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Then a stochastic process $(X_n)_{n \ge 0}$ is a Markov chain
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Then a stochastic process $(X_n)_{n \ge 0}$ is a Markov chain
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iff for every $f: E \to \R$ bounded, measurable,
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iff for every $f: E \to \R$ bounded, measurable,
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\[
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\[
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M_n(f) \coloneqq f(X_n) - f(X_0) - \sum_{j=1}^{n-1} (\mathbf{I} - \mathbf{P})f(X_j)
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M_n(f) \coloneqq f(X_n) - f(X_0) - \sum_{j=1}^{n-1} (\mathbf{I} - \mathbf{P})f(X_j)
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\]
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\]
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is a martingale
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is a martingale
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with respect to the canonical filtration of $(X_n)$.
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with respect to the canonical filtration of $(X_n)$.
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\end{theorem}
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\end{theorem}
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\begin{proof}
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\begin{proof}
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$\implies$
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$\implies$
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Fix some bounded, measurable $f : E \to \R$.
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Fix some bounded, measurable $f : E \to \R$.
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Then, for all $n$, $M_n(f)$ is bounded
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Then, for all $n$, $M_n(f)$ is bounded
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and hence $M_n(f) \in L^1$.
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and hence $M_n(f) \in L^1$.
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$M_n(f)$ is $\cF_n$-measurable for all $n \in \N$.
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$M_n(f)$ is $\cF_n$-measurable for all $n \in \N$.
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\begin{claim}
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$\bE[M_{n+1}(f) | \cF_n] = M_n(f)$.
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\end{claim}
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\begin{subproof}
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It suffices to show
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$\bE[M_{n+1}(f) - M_n(f) | \cF_n] = 0$ a.s.
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We have
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In order to prove $\bE[M_{n+1}(f) | \cF_n] = M_n(f)$,
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\begin{IEEEeqnarray*}{rCl}
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it suffices to show $\bE[M_{n+1}(f) - M_n(f) | \cF_n] = 0$ a.s.
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\bE[M_{n+1}(f) - M_n(f) | \cF_n]
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&=& \bE[f(X_{n+1} | \cF_n] - (\mathbf{P}f)(X_n)\\
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&\overset{\text{Markov property}}{=}& (\mathbf{P}f)(X_n) - (\mathbf{P}f)(X_n)\\
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&=& 0
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\end{IEEEeqnarray*}
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\end{subproof}
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$\impliedby$
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We have
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\begin{IEEEeqnarray*}{rCl}
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\bE[M_{n+1}(f) - M_n(f) | \cF_n]
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&=& \bE[f(X_{n+1} | \cF_n] - (\mathbf{P}f)(X_n)\\
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&\overset{\text{Markov property}}{=}& (\mathbf{P}f)(X_n) - (\mathbf{P}f)(X_n)\\
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&=& 0
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\end{IEEEeqnarray*}
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$\impliedby$
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Suppose $(M_n(f))_n$ is a martingale for all bounded, measurable $f$.
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Suppose $(M_n(f))_n$ is a martingale for all bounded, measurable $f$.
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By the martingale property, we have
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By the martingale property, we have
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\begin{IEEEeqnarray*}{rCl}
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\begin{IEEEeqnarray*}{rCl}
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