\[
\newcommand{\C}{\mathbb{C}}
\newcommand{\haar}{\mathsf{m}}
\newcommand{\P}{\mathcal{P}}
\newcommand{\R}{\mathbb{R}}
\newcommand{\N}{\mathbb{N}}
\newcommand{\Z}{\mathbb{Z}}
\newcommand{\d}{\mathsf{d}}
\newcommand{\g}{>}
\newcommand{\l}{<}
\newcommand{\intd}{\,\mathsf{d}}
\newcommand{\Re}{\mathsf{Re}}
\newcommand{\area}{\mathop{\mathsf{Area}}}
\newcommand{\met}{\mathop{\mathsf{d}}}
\newcommand{\emptyset}{\varnothing}
\newcommand{\B}{\mathscr{B}}
\newcommand{\fou}{\mathcal{F}}
\]
Fix probabilities
\[
p(0) + p(1) = 1
\qquad
q(0,0) + q(0,1) = 1 = q(1,0) + q(1,1)
\]
with
\[
\begin{bmatrix} p(0) & p(1) \end{bmatrix} \begin{bmatrix} q(0,0) & q(0,1) \\ q(1,0) & q(1,1) \end{bmatrix} = \begin{bmatrix} p(0) & p(1) \end{bmatrix}
\]
- For which values does the corresponding Markov measure
\[
\mu([\epsilon_1 \cdots \epsilon_r]) = p(\epsilon_1) q(\epsilon_1,\epsilon_2) \cdots q(\epsilon_{r-1},\epsilon_r)
\]
satisfy $\mu(Y) = 1$? What are those measures?
- Come up with a four-vertex graph and a Markov measure $\eta$ on $\{0,1,2,3\}^\N$ and a map
\[
\pi : \{0,1,2,3\}^\N \to \{0,1\}^\N
\]
such that $\xi = \eta \circ \pi^{-1}$ is a measure on $\{0,1\}^\N$ with $\xi(Y) = 1$.