3.1 Statement and structure of the proof

Theorem 3.1 Suppose that the stationary measure ${\mathbb P}$ satisfies ID, FE, and KB and that the ergodic measure ${\mathbb Q}$ satisfies ID and FE.Footnote ⁵ Then,

$$\begin{align*}\lim_{N\to\infty} \widehat{Q}_N(y,x) = h_{\text{c}}({\mathbb Q}|{\mathbb P}) \end{align*}$$

for $({\mathbb P}\otimes {\mathbb Q})$ -almost every $(x,y)$ .

Let us now provide the structure of the proof in the case where , postponing the more technical aspects to Sections 3.2 and 3.3. Throughout,

$$\begin{align*}{ \ell_{-,N}} := \frac{\ln N}{-2\gamma_-} \end{align*}$$

and

$$\begin{align*}{ \ell_{+,N}} := \frac{2\ln N}{-\gamma_+}. \end{align*}$$

These will serve as a priori bounds on the lengths of the words in different auxiliary parsings.

• Upper bound. Let $\epsilon \in (0, \tfrac 12)$ and be arbitrary. We consider an auxiliary sequential parsing $y_1^N= y^{(1,N)} y^{(2,N)} \dotsc y^{(\widehat {c}_N,N)}$ , where each word $y^{(j,N)}$ has length $\ell _{j,N}$ and is – except possibly for $y^{(\widehat c_N,N)}$ – the shortest prefix of $T^{\ell _{1,N}+\dots +\ell _{j-1,N}}y_1^N$ satisfying

  (3.1) $$ \begin{align} {\mathbb P}[y^{(j,N)}] \leq N^{-1+\epsilon}, \end{align} $$
  where we define and for any $k<N$ . The power is chosen in the hope that the words in this auxiliary parsing will be long enough, yet likely enough for ${\mathbb P}$ that the vast majority of them find a match in $x_1^N$ . To motivate this Ansatz, note that, by linearity of expectation, the expected number of times a given string of ${\mathbb P}$ -probability $N^{-1+\epsilon }$ appears in a string of length N obtained from ${\mathbb P}$ grows as $N^{\epsilon }$ .

  For N large enough, each length $\ell _{j,N}$ is between $\ell _{-,N}$ and $\ell _{+,N}$ , due to Properties FE and SE, except possibly for $\ell _{\widehat {c}_N,N}$ which need only satisfy the upper bound. In particular, $\widehat {c}_N = O(\frac {N}{\ln N})$ .

  Note that, for each $j = 1,2, \dotsc , \widehat c_N$ , the appearance of $y^{(j,N)}$ as a substring of $x_1^N$ – written $y^{(j,N)} \in x_1^N$ in what follows – implies the presence of at most one separator of the original ZM parsing within $y^{(j,N)}$ , that is,

  $$\begin{align*}{\mathbb P}\left\{ x : \#\{ j \leq \widehat{c}_N : y^{(j,N)} \in x_1^N\} = \widehat{c}_N \right\}\leq \mathbb{P}\{x\,:\, c_N(y|x) \leq \widehat c_N\}, \end{align*}$$
  which in turn implies
  $$\begin{align*}\mathbb{P}\{x\,:\, c_N(y|x)> \widehat c_N\}\leq {\mathbb P}\left\{ x : \#\{ j \leq \widehat{c}_N : y^{(j,N)} \notin x_1^N\} > 0 \right\}. \end{align*}$$
  We show in Lemma 3.3 that the probability on the right-hand side is summable in N and hence
  (3.2) $$ \begin{align} \sum_{N=1}^\infty{\mathbb P}\left\{ x : c_N(y|x)> \widehat c_N \right\}<\infty. \end{align} $$

  On the other hand, Lemma 3.10 below shows that

  $$ \begin{align*} (-1+\epsilon) (\widehat{c}_N-1) \ln N &= \sum_{j=1}^{\widehat{c}_N-1} \ln N^{-1+\epsilon} \\ &\geq \sum_{j=1}^{\widehat{c}_N} \ln {\mathbb P}[y^{(j,N)}] \\ &\geq \ln {\mathbb P}[y_1^N] - o(N). \end{align*} $$
  Hence,
  $$ \begin{align*} &\mathbb{P}\left\{x\,:\, c_N(y|x)\ln N+\ln \mathbb{P}[y_1^N]>-\frac\epsilon{1-\epsilon}\ln \mathbb{P}[y_1^N]+\ln N + \epsilon N \right\} \\ &\quad\qquad\qquad\quad\qquad\qquad\qquad\qquad \leq \mathbb{P}\left\{x\,:\, c_N(y|x)\ln N>\widehat c_N\ln N \right\} \end{align*} $$
  for all N large enough. Recall that, by Condition SE, $\ln {\mathbb P}[y_1^N]\geq \gamma _- N$ with $\gamma _- < 0$ . Combining this with (3.2), we obtain
  $$ \begin{align*} &\sum_{N=1}^\infty\mathbb{P}\left\{x\,:\, \frac{c_N(y|x)\ln N}N+\frac{\ln \mathbb{P}[y_1^N]}N> -\frac{\epsilon}{1-\epsilon}\gamma_- + 2\epsilon \right\}<\infty. \end{align*} $$
  Appealing to the Borel–Cantelli lemma, using the cross entropy analogue of the Shannon–McMillan–Breiman theorem in Lemma 3.12, and then taking $\epsilon \to 0$ , we conclude that, for every , we have
  $$ \begin{align*} \limsup_{N\to\infty} \widehat{Q}_N(y,x) \leq {h^{\mathrm{c}}({\mathbb Q}|{\mathbb P})} \end{align*} $$
  for almost every x sampled from ${\mathbb P}$ .

• Lower bound I. Before we obtain the almost sure lower bound required for Theorem 3.1, let us summarize Ziv and Merhav’s argument for proving that the lower bound holds in probability. This argument will also help the reader understand the more complicated construction used for the almost sure version. Let $\epsilon \in (0, \tfrac 12)$ and be arbitrary. We consider an analogous auxiliary sequential parsing $y_1^N= y^{(1,N)} y^{(2,N)} \dotsc y^{(\bar {c}_N,N)}$ , where each word $y^{(j,N)}$ has length $\ell _{j,N}$ and is – except possibly for $y^{(\bar c_N,N)}$ – the shortest prefix of $T^{\ell _{1,N}+\dots +\ell _{j-1,N}}y_1^N$ that has probability

  (3.3) $$ \begin{align} {\mathbb P}[y^{(j,N)}] \leq N^{-1-\epsilon}, \end{align} $$
  where we define $\ell _{0,N}:=0$ . The power is chosen in the hope that the words in this auxiliary parsing will be numerous enough, yet unlikely enough for ${\mathbb P}$ that the vast majority of them find no match in $x_1^N$ . To motivate this Ansatz, note that the expected number of times a given string of ${\mathbb P}$ -probability $N^{-1-\epsilon }$ appears in a string of length N obtained from ${\mathbb P}$ decays as $N^{-\epsilon }$ .

  Again, for N large enough, each length $\ell _{j,N}$ in this parsing falls between $\ell _{-,N}$ and $\ell _{+,N}$ , due to Properties FE and SE, except possibly for the last one, which only satisfies the upper bound. In particular, $\bar {c}_N = O(\frac {N}{\ln N})$ .

  The correspondence between the parsing cardinalities $c_N(y|x)$ and $\bar c_N$ relies on the following observation: $c_N(y|x)$ must be at least equal to the number of words in the auxiliary parsing of $y_1^N$ that do not appear as strings in $x_1^N$ . Indeed, if a word $y^{(j,N)}$ does not appear as a substring of $x_1^N$ – written $y^{(j,N)} \notin x_1^N$ in what follows – then the ZM parsing has at least one separator within $y^{(j,N)}$ . That is,

  $$ \begin{align*} c_N(y|x) &\geq \#\{j\,:\,y^{(j,N)}\notin x_1^N\}\\ &\geq \#\{j\leq \bar c_N-1\,:\,y^{(j,N)}\notin x_1^N\} \end{align*} $$
  and so
  $$ \begin{align*} &{\mathbb P}\left\{x\,:\, c_N(y|x)\geq(\bar c_N-1)\left(1-\epsilon\right)\right\} \\ &\qquad \geq{\mathbb P}\left\{x\,:\, \#\{j\leq \bar c_N-1\,:\,y^{(j,N)}\in x_1^N\}\leq(\bar c_N-1)\epsilon\right\} \\ &\qquad \geq 1-{\mathbb P}\left\{x: \#\{j\leq \bar c_N-1\,:\,y^{(j,N)}\in x_1^N\}> (\bar{c}_N-1)\, \epsilon\right\}. \end{align*} $$
  One can easily show using a crude union bound and Markov’s inequality that the appearance in $x_1^N$ of more than an arbitrarily small proportion of all the words in the auxiliary parsing except for the last one has vanishing – but not necessarily summable – probability, and this enables us to conclude that
  (3.4) $$ \begin{align} \lim_{N\to\infty}{\mathbb P}\left\{x : c_N(y|x)\geq(\bar c_N-1)(1-\epsilon)\right\}=1. \end{align} $$

  Note that since, by construction, for any $j = 1,2, \dotsc , \bar c_N$ , the auxiliary word $y^{(j,N)}$ has no strict prefix with probability less than $N^{-1-\epsilon }$ , the lower bound in Condition ID implies that $ {\mathbb P}[y^{(j,N)}] \geq N^{-1-2 \epsilon } $ for N large enough. Therefore, Lemma 3.10 yields

  (3.5) $$ \begin{align} (-1-2\epsilon) \bar{c}_N \ln N-o(N) &\leq \sum_{j=1}^{\bar{c}_N} \ln {\mathbb P}[y^{(j,N)}]\nonumber\\ &\leq \ln {\mathbb P}[y_1^N] + o(N), \end{align} $$
  which together with (3.4) implies
  $$\begin{align*}\lim_{N\to \infty}{\mathbb P}\left\{x : \frac{c_N(y|x)\ln N}N +\frac{\ln{\mathbb P}[y_1^N]}N\geq -{{3}}\epsilon\frac{\bar{c}_N\ln N}{N}-\epsilon\right\}=1, \end{align*}$$

  Thus, using Lemma 3.12, the fact that $\bar {c}_N=O(\frac {N}{\ln N})$ and taking $\epsilon \to 0$ , we conclude that, for all , we have

  (3.6) $$ \begin{align} h^{\mathrm{c}}({\mathbb Q}|{\mathbb P}) \leq \liminf_{N\to\infty} \widehat{Q}_N(y,x) \end{align} $$
  in probability with respect to x sampled from ${\mathbb P}$ .

• Lower bound II. Let $\epsilon \in (0, \tfrac 12)$ and be arbitrary, and fix $0 < \alpha <\frac {\gamma _+}{8\gamma _-} < 1$ . In what follows and in the last part of Section 3.2, the number $N^{\alpha }$ is to be understood as its integer part $\lfloor N^{\alpha }\rfloor $ . Following Ziv and Merhav’s original strategy for strengthening convergence in probability to almost sure convergence, we modify the auxiliary parsing of $y_1^{N}$ in “Lower Bound I” by applying the same algorithm separately to subsequent blocks of $N^\alpha $ symbols of $y_1^{N}$ .

  First, let $y^{(1,1,N)}$ be the shortest prefix of $y_1^{N^{\alpha }}$ such that ${\mathbb P}[y^{(1,1,N)}] \leq N^{-1-\epsilon }$ ; it has length $\ell _{1,1,N}$ , between $\ell _{-,N}$ and $\ell _{+,N}$ for N large enough due to Properties FE and SE. Now, let $y^{(2,1,N)}$ be the shortest prefix of $y_{\ell _{1,1,N}+1}^{N^{\alpha }}$ such that ${\mathbb P}[y^{(2,1,N)}] \leq N^{-1-\epsilon }$ , and so on until not possible. We have parsed a first block of size $N^\alpha $ :

  $$ \begin{align*} y_1^{N^\alpha} = y^{(1,1,N)} y^{(2,1,N)} \dotsb y^{(d_{1,N},1,N)} \xi^{(1,N)}, \end{align*} $$
  where the (possibly empty) buffer $\xi ^{(1,N)}$ has probability at least $N^{-1-\epsilon }$ and length at most $\ell _{+,N}$ due to Property FE.

  We then repeat the procedure with $T^{N^\alpha }y_1^N$ to obtain the second block, and so on until

  (3.7) $$ \begin{align} &y_1^{N}= y^{(1,1,N)} y^{(2,1,N)} \dotsb y^{(d_{1,N},1,N)} \xi^{(1,N)} y^{(1,2,N)} y^{(2,2,N)} \dotsb y^{(d_{2,N},2,N)} \xi^{(2,N)} \nonumber\\ &\qquad\qquad\qquad\qquad\quad \dotsb y^{(1,M_N,N)} y^{(2,M_N,N)} \dotsb y^{(d_{M_N,N},M_N,N)} \xi^{(M_N,N)}. \end{align} $$
  The construction of $y^{(1,M_N,N)} y^{(2,M_N,N)} \dotsb y^{(d_{M_N,N},M_N,N)} \xi ^{(M_N,N)}$ may differ from that of the previous $y^{(1,s,N)} y^{(2,s,N)} \dotsb y^{(d_{s,N},s,N)} \xi ^{(s,N)}$ for $s < M_N$ in that it might be the parsing of a block of a length smaller than $N^\alpha $ if there is a remainder in the division of N by $N^\alpha $ . Note that, for N large enough, $N^{1-\alpha }\leq M_N \leq 2N^{1-\alpha }$ and . The number of auxiliary parsed words to be considered is
  $$\begin{align*}\tilde{c}_N := d_{1,N} + d_{2,N} + \dotsb +d_{M_N,N}. \end{align*}$$
  It follows from the above that $\tilde {c}_N=O(\frac {N}{\ln N})$ , since for any $s<M_N$ . As explained in “Lower bound I,” $c_N(y|x)$ must be at least equal to the number of words in the auxiliary parsing of $y_1^N$ that do not appear as strings in $x_1^N$ , that is,
  $$\begin{align*}c_N(y|x)\geq \sum_{s = 1}^{M_N} \#\left\{j : y^{(j,s,N)} \notin x_1^N\right\}. \end{align*}$$

  In order to control the latter, we prove below the two following technical estimates:

  • • Proposition 3.6: For almost every y sampled from ${\mathbb Q}$ , there exists $N_\epsilon (y)$ such that, for $N \geq N_\epsilon (y)$ , the number of indices s such that the words $y^{(1,s,N)}$ , $y^{(2,s,N)}, \dotsc , y^{(d_{s,N},s,N)}$ are not distinct is smaller than $\epsilon M_N$ ; we comment on the reason for this technical consideration in Remark 3.8.

  • • Proposition 3.9: Denoting by $\mathcal {S}_{\mathrm {g}}(y_1^N)$ the set of indices s whose block of $y_1^N$ does consist of distinct words, we have

    $$ \begin{align*} {\mathbb P}\{\#\{j : y^{(j,s,N)} \in x_1^N\}> \epsilon {d_{+,N}}\} \leq {\ell_{+,N}}^2 \mathrm{e}^{\frac{\epsilon^2 \gamma_+ }{8}\frac{d_{+,N}}{\ell_{+,N}}} \end{align*} $$
    for N large enough and all $s \in \mathcal {S}_{\mathrm {g}}(y_1^N)$ . This means that, with high probability, only a small fraction of the words in these “good blocks” can appear in $x_1^N$ (and fail to contribute to $c_N(y|x)$ ).

  Therefore, even considering the worst-case scenario where all $y^{(i,s,N)}$ with $s\notin \mathcal {S}_{\mathrm {g}}(y_1^N)$ do appear in $x_1^N$ , we find that, for almost every y sampled from ${\mathbb Q}$ ,

  $$ \begin{align*} &\sum_{N = N_\epsilon(y)}^\infty {\mathbb P}\left\{x\,:\, c_N(y|x) <\tilde{c}_N - 2\epsilon {d_{+,N}}M_N \right\}\\ & \quad\leq \sum_{N = N_\epsilon(y)}^\infty M_N \max_{s \in \mathcal{S}_{\mathrm{g}}(y_1^N)}{\mathbb P}\{\#\{j : y^{(j,s,N)} \in x_1^N\}> \epsilon {d_{+,N}}\}<\infty. \end{align*} $$
  In fact,
  $$ \begin{align*} & \left\{x\,:\, \sum_{s = 1}^{M_N} \#\left\{j : y^{(j,s,N)} \notin x_1^N\right\}< \tilde c_N-2\epsilon { d_{+,N}} M_N\right\} \\ &\hspace{1cm}= \left\{x\,:\, \sum_{s = 1}^{M_N} \#\left\{j : y^{(j,s,N)} \in x_1^N\right\}> 2\epsilon {d_{+,N} }M_N\right\} \\ &\hspace{1cm}\subseteq \left\{x\,:\, \sum_{s \in \mathcal{S}_{\mathrm{g}}(y_1^N)} \#\left\{j : y^{(j,s,N)} \in x_1^N\right\}> \epsilon {d_{+,N} }M_N\right\}, \end{align*} $$
  and we can perform a union bound after observing that for the number of words in the auxiliary parsing of $y_1^N$ that appear in $x_1^N$ and belong to “good blocks” to exceed $\epsilon {d_{+,N}}M_N$ , at least the number of such words in one of the “good blocks” must exceed $\epsilon d_{+,N}$ .

  Appealing to Lemma 3.10 and Remark 3.11, the relation (3.5) between $\tilde {c}_N$ and $\ln {\mathbb P}[y_1^N]$ remains valid and yields

  $$ \begin{align*} & {\mathbb P}\left\{x: \frac{c_N(y|x)\ln N}N +\frac{\ln {\mathbb P}[y_1^N]}N<-2\epsilon \frac{\tilde{c}_N\ln N}N-\epsilon-8\epsilon\frac{ \ln N}{\ell_{-,N}} \right\}\\ &\qquad\qquad\qquad\qquad \quad\leq {\mathbb P}\left\{x: \frac{c_N(y|x)\ln N}N <\frac{\tilde{c}_N\ln N}N - 2\epsilon {d_{+,N}}M_N\frac{\ln N}N \right\} \end{align*} $$
  for N large enough, which implies that there exists some constant $C=C(\gamma _-)>0$ such that
  $$\begin{align*}\sum_{N=1}^\infty{\mathbb P}\left\{x: \frac{c_N(y|x)\ln N}N +\frac{\ln {\mathbb P}[y_1^N]}N< -C\epsilon \right\}<\infty. \end{align*}$$
  By Lemma 3.12 and the Borel–Cantelli lemma, taking $\epsilon \to 0$ , we conclude that
  $$ \begin{align*} h^{\mathrm{c}}({\mathbb Q}|{\mathbb P}) \leq \limsup_{N\to\infty} \widehat{Q}_N(y,x) \end{align*} $$
  for almost every $(x,y)$ sampled from ${\mathbb P}\otimes {\mathbb Q}$ .

The above strategy is essentially that of Ziv and Merhav, but the lemmas and propositions on which it relies need to be adapted beyond Markovianity. Before we do so, let us state and prove a proposition that justifies our focus on situations where .

Proof Fix k as in the hypothesis and then . Because , a crude counting argument yields that the ZM parsing satisfies

$$\begin{align*}c_N(y|x) \geq \frac{\#\{j \leq N-k+1 : T^{j-1}y \in [a]\}}{k} \end{align*}$$

for all . Because and k is fixed, Birkhoff’s ergodic theorem applied to the function $\mathbf {1}_{[a]}$ yields

$$\begin{align*}\liminf_{N\to\infty} \frac{c_N(y|x)}{N}> 0, \end{align*}$$

for almost every $y\sim {\mathbb Q}$ . This allows us to conclude that, almost surely, the estimator diverges.

As for the claim that this is in agreement with Theorem 3.1, it is based on the observation that if , then for all $n \geq k$ . Since the existence of such that ${\mathbb P}_n[a] = 0$ causes at least one summand to be infinite on the right-hand side of (2.1), this allows us to conclude that $h_{\text {c}}({\mathbb Q}|{\mathbb P}) = \infty $ .

3.2 Properties of the auxiliary parsings

Throughout this section, $\epsilon \in (0,1/2)$ is fixed but arbitrary. We assume that ${\mathbb P}$ and ${\mathbb Q}$ are stationary and satisfy . For readability, we will omit keeping track of the N-dependence in some of the notation introduced above. As foreshadowed in the introduction, our analysis of the cardinalities of the auxiliary parsings will use reformulations in terms of waiting times.

Lemma 3.3 Suppose that ${\mathbb P}$ satisfies ID, FE, and KB. Let be arbitrary and consider the auxiliary parsing of $y_1^N$ built around the requirement (3.1). Then,

$$\begin{align*}{\mathbb P}\left\{ x : \#\{ j \leq \widehat{c}_N : y^{(j)} \notin x_1^N\}> 0 \right\} \leq N\mathrm{e}^{-\frac{N^{\frac{\epsilon}{4}}}{3\ell_+}} \end{align*}$$

for N large enough.

Proof Let $\underline {y}^{(j)}$ be the word that is obtained by removing the last letter from $y^{(j)}$ ; by construction, ${\mathbb P}[\underline {y}^{(j)}]> N^{-1+\epsilon }$ . So, in view of ID,

(3.8)

for N large enough. We have used the fact that $k_{\ell _j-1} \leq k_{\ell _+}$ with $k_\ell = o(\ell )$ and ${\ell _+=O(\ln N)}$ . Using KB and considering all N large enough, we have

(3.9) $$ \begin{align} {\mathbb P}\{ x\,:\,W_{\ell_j}(y^{(j)},x)>N-\ell_j+1 \} &\leq \exp\left(-\frac{N^{\frac{\epsilon}{4}}}{2\ell_++\tau_{\ell_+}}\right), \end{align} $$

where we used the defining properties of $k_{\ell _j}$ and $\ell _j$ . Then, using that for N large enough we have $\tau _{\ell _+}\leq \ell _+$ and taking a union bound over j,

$$\begin{align*}{\mathbb P}\left(\bigcup_{j=1}^{\widehat c_N}\{x : W_{\ell_j}(y^{(j)},x)>N-\ell_j+1\} \right) \leq N\exp\left(-\frac{N^{\frac{\epsilon}{4}}}{3\ell_+}\right). \end{align*}$$

To conclude, note that $W_{\ell _j}(y^{(j)},x)>N-\ell _j+1$ is a necessary and sufficient condition for $y^{(j)} \notin x_1^N$ .

While, on the one hand, the last lemma states that the words in the auxiliary parsing built around (3.1) tend to appear in $x_1^N$ , one can show that, on the other hand, the words in the auxiliary parsing built around (3.3) tend to not appear in $x_1^N$ . However, the probabilistic estimate obtained pursuing this strategy only achieves convergence in probability of the ZM estimator; see “Lower Bound I.” As Ziv and Merhav showed in their original paper in the Markovian case, this estimate can actually be refined and made summable in N using some additional combinatorial and probabilistic arguments. Such a refinement is used to go from convergence in probability to almost sure convergence in Section 3.1. We recall the following basic facts about our modified auxiliary parsing (3.7) for N large enough:

• • there are $M_N \leq 2N^{1-\alpha }$ blocks, indexed by s, each of length $N^\alpha $ except for the last one $(s=M_N)$ which possibly has length less than $N^\alpha $ ;

• • the sth block contains $d_{s}$ words $y^{(i,s)}$ with

  
  except for the last one $(s=M_N)$ for which the lower bound may not apply, and one (possibly empty) buffer $\xi ^{(s)}$ ;

• • each word $y^{(i,s)}$ has length $\ell _{i,s}$ , with

  $$\begin{align*}\ell_- := \frac{\ln N}{-2\gamma_-} \leq \ell_{i,s} \leq \frac{2\ln N}{-\gamma_+} =: \ell_+. \end{align*}$$

Most of the factors of 2 in these facts are suboptimal; they are only meant to avoid having to consider integer parts or superficial dependence on $\epsilon $ .

Lemma 3.4 If ${\mathbb Q}$ satisfies ID and FE, then

$$\begin{align*}{\mathbb Q}\{y : s \in \mathcal{S}_{\mathrm{b}}(y_1^N)\} \leq \mathrm{e}^{k_{\ell_-}}N^{-2\alpha}, \end{align*}$$

for every s and every N large enough.

Proof Fix ${\mathbb Q}$ as in the statement. By shift invariance, ${\mathbb Q}\{y : s \in \mathcal {S}_{\mathrm {b}}(y_1^N) \} \leq {\mathbb Q}\{y : 1 \in \mathcal {S}_{\mathrm {b}}(y_1^N)\}$ .Footnote ⁶ For the first block to be bad, two words $y^{(i,1)}$ and $y^{(j,1)}$ need to coincide, and in particular, their $\ell _-$ -prefixes need to coincide. Hence, considering all possible starting indices of these two words, and appealing to shift-invariance, ID and FE, we derive

To conclude, recall that we have chosen $\alpha <\frac {\gamma _+}{8\gamma _-} = - \frac {\gamma _+\ell _-}{4\ln N}$ and that $\gamma _\pm < 0$ .

Lemma 3.5 If ${\mathbb Q}$ satisfies ID and FE, then

$$\begin{align*}{\mathbb Q}\{y : \#\mathcal{S}_{\mathrm{b}}(y_1^N) = m \}\leq \binom{M_N}{m}\mathrm{e}^{2mk_{\ell_-}}N^{-2m\alpha}, \end{align*}$$

for all $m\in {\mathbb N}$ and for all N large enough.

Proof Fix ${\mathbb Q}$ and m as in the statement. Let us first consider the probability that the blocks of $y_1^N$ labeled $s_m$ , $s_{m-1}$ down to $s_1$ are bad. This event can be thought of as mth in a sequence of events defined inductively by $E^{\prime }_{k+1}=T^{-N^{\alpha }(s_{k+1}-1)}\{1 \in \mathcal {S}_{\mathrm {b}}\} \cap E^{\prime }_k$ where $E^{\prime }_0=\Omega $ . It follows, by a straightforward adaptation of the strategy of Lemma 3.4, that

Iterating and accounting for the different choices of $s_1, \dotsc , s_{m-1}, s_m$ (recall that $s \leq M_N$ ) gives the proposed bound.

Proof Fixing ${\mathbb Q}$ as in the statement, using Markov’s inequality, the binomial theorem, and Lemma 3.5, for every $b>0$ , we have

$$ \begin{align*} {\mathbb Q}\left\{y: \#\mathcal{S}_{\mathrm{b}}(y_1^N) \geq \epsilon M_N\right\}&\leq \mathbb{E}\left(\mathrm{e}^{b(\#\mathcal{S}_{\mathrm{b}}(y_1^N)) }\right)\mathrm{e}^{-b\epsilon M_N}\\ &=\mathrm{e}^{-b\epsilon M_N}\sum_{m=1}^{M_N}\mathrm{e}^{bm}{\mathbb Q}\left\{y: \#\mathcal{S}_{\mathrm{b}}(y_1^N) = m \right\}\\ &\leq\mathrm{e}^{-b\epsilon M_N}\left(1+\mathrm{e}^{b+2k_{\ell_-}}N^{-2\alpha}\right)^{M_N}. \end{align*} $$

Choosing $b=2\alpha \ln N-2k_{\ell _-}$ , recalling that $M_N/N^{1-\alpha }\in (1,2)$ , and considering N large enough so that $b>0$ gives the bound

(3.10) $$ \begin{align} {\mathbb Q}\left\{y: \#\mathcal{S}_{\mathrm{b}}(y_1^N) \geq \epsilon M_N\right\}\leq \mathrm{e}^{-N^{1-\alpha}(2\alpha\epsilon\ln N-2\epsilon k_{\ell_-}-2\ln 2)}. \end{align} $$

The proposition thus follows from the Borel–Cantelli lemma.

Lemma 3.7 Suppose that ${\mathbb P}$ satisfies ID and that the sth block of $y_1^N$ is good. Given $\ell $ and $K\in \{1,2,\dotsc , \ell \}$ ,

$$ \begin{align*} & {\mathbb P}\left\{x:\#\left\{j : y^{(j,s)}=x_{K+r\ell}^{K+r\ell+(\ell-1)}\text{ for some } r \in \left\{0, 1, \dots,\left\lfloor\frac{N-K+1}{\ell}\right\rfloor-1\right\} \right\}=m\right\} \\ & \quad\leq \binom{d_+}{m}\mathrm{e}^{mk_{\ell_+}}N^{-m\epsilon}. \end{align*} $$

Proof By shift invariance, we can assume that $s=1$ . Consider a set $I=\{i_k\}_{k=1}^m$ of m distinct indices such that $y^{(i_k,1)}$ has length $\ell $ , and let $F(I)$ denote the event that all the words $\{y^{(i_k,1)}\}_{k=1}^m$ have a match in $x_1^N$ with a starting point equivalent to K mod $\ell $ . Since the words $\{y^{(i_k,1)}\}_{k=1}^m$ are distinct, the starting positions of the matches considered must be distinct. Moreover, by assumption, each such starting position is of the form $r\ell + K$ for some r at most $\lfloor \tfrac {N-K+1}{\ell }\rfloor - 1$ . Therefore, enumerating all possibilities, we find

$$ \begin{align*} F(I) \subseteq \bigcup_{r_1,\dotsc,r_{m}}\bigcap_{k=1}^{m}T^{-r_k\ell-K }[y^{(i_{k},\ell)}], \end{align*} $$

where the union is taken over distinct nonnegative integers $r_1, \dotsc , r_m$ all at most $\lfloor \tfrac {N-K+1}{\ell }\rfloor -1$ . Using ID, shift invariance, and subadditivity gives

$$ \begin{align*} {\mathbb P}(F(I)) &\leq m!\binom{\lfloor \frac{N-K+1}{\ell}\rfloor-1}{m}\left(\mathrm{e}^{k_{\ell_+}}\max_{i \in I}{\mathbb P}[y^{(i,\ell)}]\right)^m \\ & \leq N^m(\mathrm{e}^{k_{\ell_+}}N^{-1-\epsilon})^m \\ & \leq \mathrm{e}^{mk_{\ell_+}}N^{-m\epsilon}. \end{align*} $$

To conclude, we use a union bound, together with an upper bound on the number of sets I of this nature.

Proposition 3.9 Let be arbitrary and consider the modified auxiliary parsing of $y_1^N$ in (3.7). Suppose that ${\mathbb P}$ satisfies ID and that the sth block of $y_1^N$ is good. Then, for N large enough, the event that more than a fraction $\epsilon $ of the maximum number $d_+$ of words $y^{(i,s)}$ in the sth block appears in $x_1^N$ satisfies

(3.11) $$ \begin{align} {\mathbb P}\{x:\#\{j : y^{(j,s)} \in x_1^N\}> \epsilon d_+\}\leq \ell_+^2\mathrm{e}^{ \frac{\gamma_+\epsilon^2}8\frac{d_+}{\ell_+}}. \end{align} $$

Proof Fix $s\in \mathcal {S}_{\mathrm {g}}(y_1^N)$ . Given $\ell $ and $K \in \{1,\dotsc ,\ell \}$ , consider

(3.12) $$ \begin{align} \chi_{(K,\ell)} := \sum_{i : \ell_{i,s} = \ell} \mathbf{1}_{W_\ell(y^{(i,s)},\,\cdot\,)\leq N} \cdot \mathbf{1}_{W_\ell(y^{(i,s)},\,\cdot\,) \equiv_{\operatorname{mod} \ell} K}. \end{align} $$

Observe that, for any fixed x,

$$\begin{align*}\#\{j : y^{(j,s)} \in x_1^N\} \leq \sum_{i=1} ^{d_{s}}\mathbf{1}_{W_{\ell_{i,s}}(y^{(i,s)},x)\leq N}, \end{align*}$$

and so for the random variable in (3.11) to exceed $\epsilon d_+$ , at least one of the random variables $\chi _{(K,\ell )}$ defined by (3.12) must exceed $\tfrac {\epsilon d_+}{\ell _+^2}$ , that is,

(3.13) $$ \begin{align} {\mathbb P}\{x:\#\{j : y^{(j,s)} \in x_1^N\}> \epsilon d_+\} &\leq{\mathbb P}\left( \bigcup_{(K,\ell)} \left\{x: \chi_{(K,\ell)}(x)> \epsilon\frac{d_+}{\ell_+^2 } \right\} \right). \end{align} $$

Following the same strategy as in the proof of Proposition 3.6, we use Markov’s inequality, the binomial theorem, and Lemma 3.7 to derive that, for every $b>0$ ,

$$\begin{align*}{\mathbb P}\left\{x: \chi_{(K,\ell)}(x)> \epsilon\frac{d_+}{\ell_+^2} \right\}\leq \left(1+\frac{\mathrm{e}^{b+k_{\ell_+}}}{N^{\epsilon}}\right)^{d_+}\mathrm{e}^{-b\epsilon\frac{d_+}{\ell_+^2 }}. \end{align*}$$

Choosing $b=\frac {\epsilon }{2}\ln N$ yields

$$ \begin{align*} {\mathbb P}\left\{x: \chi_{(K,\ell)}(x)> \epsilon\frac{d_+}{\ell_+^2} \right\} &\leq \mathrm{e}^{ \frac{\gamma_+\epsilon^2}4 \frac{d_+}{\ell_+}(1-o(1))} \\ &\leq \mathrm{e}^{ \frac{\gamma_+\epsilon^2}8\frac{d_+}{\ell_+}} \end{align*} $$

for N large enough, recalling that $\gamma _+<0$ . Going back to our observation (3.13), we conclude the proof by performing a union bound over K and $\ell $ .

3.3 Cross entropy

Lemma 3.10 If ${\mathbb P}$ satisfies ID, , and $y_1^N$ is parsed as

$$\begin{align*}y_1^N = y^{(1,N)} y^{(2,N)} \dotsc y^{(c^{\prime}_N-1,N)} y^{(c^{\prime}_N,N)}, \end{align*}$$

with for some properly diverging, nonnegative sequence $(\lambda _N)_{N=1}^\infty $ , then

$$\begin{align*}\sum_{j=1}^{c^{\prime}_N} \ln {\mathbb P}[y^{(j,N)}] = \ln {\mathbb P}[y_1^N] + o(N). \end{align*}$$

Proof Suppose ${\mathbb P}$ satisfies ID, , and $y_1^N$ is parsed as in the statement. Both the upper and lower bounds are proved similarly, so we only provide the proof of the former. Let $\epsilon> 0$ be arbitrary and note that ID yields

$$ \begin{align*} \ln {\mathbb P}[y_1^N]&=\ln {\mathbb P}[y^{(1,N)} y^{(2,N)} \dotsc y^{(c^{\prime}_N-1,N)} y^{(c^{\prime}_N,N)}]\\ &\leq \ln \left(\mathrm{e}^{k_{\ell_1}+\dots+k_{\ell_{c^{\prime}_N-1}}}{\mathbb P}[y^{(1,N)}]{\mathbb P}[ y^{(2,N)}] \dotsc {\mathbb P}[ y^{(c^{\prime}_N,N)}]\right)\\ &=\sum_{j=1}^{c^{\prime}_N}\ln {\mathbb P}[y^{(j,N)}]+\sum_{j=1}^{c^{\prime}_N-1}k_{\ell_j}. \end{align*} $$

Now since $k_\ell = o(\ell )$ and $\lambda _N \to \infty $ , we have $k_{\ell _j} < \epsilon \ell _{j}$ for N large enough. Therefore,

$$ \begin{align*} \ln {\mathbb P}[y_1^N] &< \sum_{j=1}^{c^{\prime}_N}\ln {\mathbb P}[y^{(j,N)}]+\sum_{j=1}^{c^{\prime}_N-1}\epsilon {\ell_j} \\ &< \sum_{j=1}^{c^{\prime}_N}\ln {\mathbb P}[y^{(j,N)}] + \epsilon N \end{align*} $$

for N large enough.

Lemma 3.12 If ${\mathbb P}$ satisfies ID and ${\mathbb Q}$ is ergodic, and if , then

$$\begin{align*}-\ln {\mathbb P}[y_1^N] = Nh_{\text{c}}({\mathbb Q}|{\mathbb P}) + o(N) \end{align*}$$

for ${\mathbb Q}$ -almost every y.

3.4 Comments

The following consequence of ID played an important role in the proof of the upper bound:

• Ad For every $n \in {\mathbb N}$ , the bound

  (3.14)

  
  holds.

Indeed, by construction of Ziv and Merhav’s auxiliary parsings, there is a lower bound on ${\mathbb P}[\underline {y}^{(j,N)}]$ and an upper bound on ${\mathbb P}[y^{(j,N)}]$ , but both the bounds (3.2) and (3.5) require a lower bound on ${\mathbb P}[{y^{(j,N)}}]$ (see Lemma 3.3). Condition Ad serves as a way of going back and forth between the two. Unfortunately, Ad may fail upon relaxing the lower bound in ID to the more general lower-decoupling conditions that have met with success in tackling other related problems [Reference Benoist, Cuneo, Jakšić and PilletBCJP21, Reference Cristadoro, Degli Esposti, Jakšić and RaquépasCDEJR23a, Reference Cuneo, Jakšić, Pillet and ShirikyanCJPS19, Reference Cuneo and RaquépasCR23]. We will come back to this point in Section 4.4.

As for the arguments available in the literature to establish KB, we foresee no difficulty in adapting our argument to a set of hypotheses where the roles of a and b are exchanged in the decoupling inequalities. Indeed, this would not affect SE nor Ad. While the Markov property can be equivalently written in terms of conditioning on the past or conditioning on the future, the class of g-measures discussed in Section 4 and its “reverse” counterpart do not coincide (see, e.g., [Reference Berghout, Fernández and VerbitskiyBFV19, Section 4.4]).

As mentioned in the introduction, the Ziv–Merhav estimator can be written in terms of longest-match lengths:

$$ \begin{align*} \frac{c_N \ln N}{N} &= \frac{\ln N}{\frac{1}{c_N}\sum_{i=1}^{c_N} \ell^{(i,N)}}, \end{align*} $$

whereFootnote ⁷

$$\begin{align*}\ell^{(i,N)} = \min\{\Lambda_N(T^{L^{(i-1,N)}}y,x), N-L^{(i-1,N)}\}, \end{align*}$$

with

$$ \begin{align*} L^{(0,N)} = 0 \qquad&\text{and}\qquad L^{(i,N)} = L^{(i-1,N)} + \ell^{(i,N)} \end{align*} $$

for $i = 1,2,\dotsc , c_N$ . It is known that the longest-match estimator $(\ell ^{(1,N)})^{-1}\ln N = \Lambda _N(y,x)^{-1} \ln N$ converges almost surely to the cross entropy, with good probability estimates, for a class of measures that is more general than that considered here (see [Reference KontoyiannisKon98, Section 1.3] and [Reference Cristadoro, Degli Esposti, Jakšić and RaquépasCDEJR23a, Section 3]). Hence, if each $T^{L^{(i-1,N)}}y$ were replaced by a new independent sample from ${\mathbb Q}$ , or by $T^{\Delta (i - 1)}y$ for some fixed deterministic $\Delta \in {\mathbb N}$ , then one would expect the convergence of the Ziv–Merhav estimator to also hold considerably more generally. However, the dependence structure of the starting indices seems to be posing a serious technical difficulty for the strategy of Ziv and Merhav.

4.1 Markov measures

As mentioned in Section 2, if ${\mathbb P}$ is the stationary measure for an irreducible Markov chain with positive entropy, then ${\mathbb P}$ is ergodic and satisfies ID, FE, and KB. We use this setting to illustrate the role of some of our conditions.

First, it is worth noting that the stationary measure for an irreducible Markov chain need not satisfy any form of mixing, nor the Doeblin-type condition in [Reference Kontoyiannis, Suhov and KellyKS94] because it could be periodic.

In the case of a reducible Markov chain, a stationary measure can charge two disjoint communication classes; let us call those classes ${\cal A}'$ and ${\cal A}"$ . Then, for $a \in {\cal A}'$ , the probability ${\mathbb P}\{x : W_1(a,x) \geq r\} \geq {\mathbb P}\{x : x_1 \in {\cal A}"\}$ does not decay as $r \to \infty $ . In terms of the language of subshifts, the failure of KB is due to the fact that does not satisfy any form of specification; it is a subshift of finite type that fails to be transitive. More concretely, if the sequence x starts in ${\cal A}"$ , then it remains there forever and we do not expect to be able to probe any entropic quantity that also involves the behavior of ${\mathbb P}$ on ${\cal A}'$ using the information contained in x.

Also note that a stationary measure for an irreducible Markov chain could fail to have positive entropy if, for example, it is a convex combination of Dirac masses on periodic orbits. Such a behavior is at odds with FE and can cause the bounds on the lengths of the parsed words not to be controlled in terms of $\ell _{\pm ,N}$ , a fact which was used repeatedly throughout our proofs.

4.2 Regular g-measures

Let $\Omega '$ be a topologically transitive one-sided subshift of finite type. Choosing as a starting point one particular definition in the literature among others, we will say that a translation-invariant measure ${\mathbb P}$ on $\Omega $ is a regular g-measure on $\Omega '$ if and there exists a continuous function $g: \Omega ' \to (0,1]$ such that

(4.1) $$ \begin{align} \sum_{\substack{y \in \Omega'\\ Ty = x}} g(y) = 1 \end{align} $$

for all $x\in \Omega '$ and

(4.2) $$ \begin{align} \lim_{n\to\infty} \sup_{x \in \Omega'} \left|\frac{{\mathbb P}[x_1^{n}]}{{\mathbb P}[x_2^n]} - g(x)\right| = 0. \end{align} $$

The convergence (4.2) can be used to show that ${\mathbb P}$ satisfies the decoupling condition ID (see [Reference Cuneo and RaquépasCR23, Section B.3]). Our assumption on $\Omega '$ more than suffices for ID to yield KB (see [Reference Cuneo and RaquépasCR23, Sections 3.1 and B.2]).

Note that the ratio being compared to g is continuous in x at finite n, and the k-level Markov condition, once written in terms of conditioning on the future, implies that this ratio is eventually constant in n – starting with $n=k+1$ . Hence, regular g-measures do generalize stationary k-level Markov measures.

Finally, let us discuss Condition FE in the context of regular g-measures. To do so, we will use the fact that the convergence (4.2) can also be used to establish the following weak Gibbs condition of Yuri at vanishing topological pressure: there exists an $\mathrm {e}^{o(n)}$ -sequence $(K_n)_{n=1}^\infty $ such that

$$\begin{align*}K_n^{-1} \mathrm{e}^{\sum_{j=0}^{n-1} \ln g(T^jx) } \leq {\mathbb P}[x_1^n] \leq K_n \mathrm{e}^{\sum_{j=0}^{n-1} \ln g(T^jx)} \end{align*}$$

for every $x \in \Omega '$ ; again, see [Reference Cuneo and RaquépasCR23, Section B.3], but it should be noted that this can be seen as part of the “g-measure folklore” [Reference Berghout, Fernández and VerbitskiyBFV19, Reference Olivier, Sidorov and ThomasOST05, Reference WaltersWal05]. We are now ready to provide a necessary and sufficient condition on the subshift $\Omega '$ for FE to hold for all regular g-measures on $\Omega '$ . One special case will be that regular g-measures on topologically mixing subshifts of finite type with more than one letter satisfy ID and FE, allowing for an application of our main result.

Proof Suppose that there exists r as above. Then, for every $y \in \Omega '$ , there exists $t \leq r$ such that

$$\begin{align*}g(T^{t-1} y) = 1 - \sum_{\substack{z \in \Omega'\setminus\{T^{t-1} y\} \\ Tz = T^{t}y}} g(z) \leq 1 - \delta, \end{align*}$$

where $\delta := \min g$ . This number is positive by continuity and compactness. Therefore,

$$ \begin{align*} \ln g(y) + \ln g(Ty) + \dotsb + \ln g(T^{t-1}y) \leq \ln(1-\delta) \end{align*} $$

and $\ln g(T^{t'}y) \leq \ln (1-\delta )$ for any $t'\geq t$ . But then, the weak Gibbs property yields

$$ \begin{align*} {\mathbb P}[y_1^n] &\leq K_n \mathrm{e}^{\sum_{i=0}^{\lfloor \frac nr\rfloor-1} \sum_{t=0}^{ r-1} \ln g(T^{ir+t}y)} \\ &\leq \exp\left(\ln K_n + \left\lfloor \frac nr \right\rfloor \ln(1-\delta)\right), \end{align*} $$

with $\ln K_n = o(n)$ . We conclude that Condition FE holds. Suppose now that no such r exists. Then, for every $n\in {\mathbb N}$ , there exists $y\in \Omega '$ such that $T^ty$ has only one preimage in $\Omega '$ for all $t\leq n$ . By the condition (4.1), this means that

$$\begin{align*}\ln g(y)+\ln g(Ty)+\dots+\ln g(T^{ n-1} y)=0, \end{align*}$$

which, together with the lower bound in the weak Gibbs property, implies

$$\begin{align*}{\mathbb P}[y_1^n]\geq K_n^{-1}=\mathrm{e}^{-o(n)}. \end{align*}$$

Since the right-hand side is eventually greater than $\mathrm {e}^{\gamma _+n}$ for any $\gamma _+<0$ , FE fails as well.

4.3 Statistical mechanics

Let $\overline {\Omega }'$ be a topologically transitive, two-sided subshift of finite type, and let $\Omega '$ be its one-sided counterpart. Consider a family $(\Phi _X)_{X \Subset {\mathbb Z}}$ of interactions with

• • the continuity property that, for all $X \Subset {\mathbb Z}$ , the function $\Phi _X$ – although seen as a measurable function on $\overline {\Omega }'$ – depends on the symbols with indices in the finite subset X only,

• • the translation-invariance property that, for all $X \Subset {\mathbb Z}$ , $\Phi _{X+1} = \Phi _X \circ T$ ,

• • the absolute summability property that $\sum _{\substack {X \Subset {\mathbb Z} \\ X \ni 1}} \sup _{x\in \overline {\Omega }'} |\Phi _X(x)|< \infty .$

Such interactions are considered, e.g., in [Reference RuelleRue04, Sections 1.2 and 3.1] and are colloquially said to be in “the small space.” It is well known that any equilibrium measure ${\mathbb P}$ (in the sense of the variational principle) for the energy-per-site potential

$$\begin{align*}\phi := \sum_{\substack{X \Subset {\mathbb Z} \\ \min X = 1}} {\Phi_X} \end{align*}$$

coming from such a family of interactions is a translation-invariant Gibbs state in the sense of the Dobrushin–Lanford–Ruelle equations (see, e.g., [Reference RuelleRue04, Sections 3.2 and 4.2]).Footnote ⁹ Because we are working with a sufficiently regular subshift $\overline {\Omega }'$ , the Dobrushin–Lanford–Ruelle equations and absolute summability can be used to show that ${\mathbb P}$ satisfies ID by adapting the argument of [Reference Lewis, Pfister and SullivanLPS95, Section 9] for the case $\overline {\Omega }' = {\cal A}^{\mathbb Z}$ . Again, the subshift is sufficiently regular for ID to yield KB (see [Reference Cuneo and RaquépasCR23, Sections 3.1 and B.2]).

We now turn to Condition FE, assuming a certain familiarity with the thermodynamic formalism, physical equivalence, and the Griffiths–Ruelle theorem on the reader’s part (see, e.g., [Reference RuelleRue04, Section 4]).

Every irreducible, stationary Markov measure with stochastic matrix $[P_{a,b}]_{a,b\in {\cal A}}$ can be obtained in this way by considering the following nearest-neighbor interactions on its support:

$$\begin{align*}\Phi_{\{i,i+1\}}(x) = \ln P_{x_i,x_{i+1}} \end{align*}$$

for $i\in {\mathbb N}$ and $\Phi _X(x) = 0$ for X not of the form $\{i,i+1\}$ . To see this, one can check by direct computation that, on its support, the Markov measure satisfies the Bowen–Gibbs condition for the corresponding $\phi $ . For k-level Markov measures, consider instead

$$\begin{align*}\Phi_{\{i,\dotsc,i+k-1,i+k\}}(x) = \ln \frac{{\mathbb P}[x_i\dotsc x_{i+k-1}x_{i+k}]}{{\mathbb P}[x_i\dotsc x_{i+k-1}]}. \end{align*}$$

In this sense, equilibrium measures for potentials arising from interactions that are absolutely summable do generalize stationary k-level Markov measures; we refer the reader to [Reference Barbieri, Gómez, Marcus, Meyerovitch and TaatiBGM⁺21, Reference Chandgotia, Han, Marcus, Meyerovitch and PavlovCHM⁺14] for recent thorough discussions of variants and converses to this observation. This generalization is far reaching as the theory of entropy, large deviations, and phase transition is much richer in the small space of interactions than in the space of finite-range interactions.

In a similar vein, equilibrium measures (in the sense of the variational principle on $\Omega '$ ) for abstract potentials $\phi $ in the Bowen class also satisfy ID, thanks to the Bowen–Gibbs property (see [Reference WaltersWal01, Section 4]). We refer the reader to [Reference WaltersWal01, Section 1] for a definition of the Bowen class, which can be traced back to [Reference BowenBow74]. This class includes potentials with summable variations, and thus Hölder-continuous potentials, and thus potentials naturally associated with stationary k-level Markov measures. A more complete discussion from the point of view of decoupling – including relaxation of the conditions on $\Omega '$ – can be found in [Reference Cuneo and RaquépasCR23, Section 2.3].

4.4 Hidden-Markov measures

While the above generalizations beyond Markovianity are often studied in the literature on mathematical physics and abstract dynamical systems, they might not be the most natural from an information-theoretic point of view; hidden-Markov models would most likely come to mind first for many practitioners. We recall that, among several equivalent representations, a stationary hidden-Markov measure ${\mathbb P}$ can be characterized by a tuple $(\pi , P, R)$ where $(\pi , P)$ characterizes in the usual way a stationary Markov process on a set $\mathcal {S}$ , called the hidden alphabet, and R is a $(\#\mathcal {S})$ -by- $(\#{\cal A})$ matrix whose rows each sum to 1:

$$ \begin{align*} {\mathbb P}[a_1^n] = \sum_{s_1^n \in \mathcal{S}^n} \pi_{s_1} R_{s_1, a_1} P_{s_1, s_2} R_{s_2, a_2}\cdots P_{s_{n-1}, s_{n}} R_{s_n, a_n} \end{align*} $$

for $n \in {\mathbb N}$ and $a_1^n \in {\cal A}^n$ . We restrict our attention to the case where $\mathcal {S}$ is a finite set and P is irreducible. We view the entry $R_{s,a}$ as the probability of observing $a \in {\cal A}$ at a given time step given the hidden state $s \in \mathcal {S}$ at that same time step – the dynamics of the latter governed by the hidden-Markov chain $(\pi ,P)$ . There exist only very singular examples of such measures for which FE fails. As exhibited by our next lemma, this can only happen if the process is eventually almost-surely deterministic.

Lemma 4.4 Let ${\mathbb P}$ be as above. Then, ${\mathbb P}$ satisfies FE if and only if, for each $s \in \mathcal {S}$ , there exists L such that

$$\begin{align*}\#\{a \in {\cal A}^L : {\mathbb P}[a | s_1 = s]> 0 \} > 1. \end{align*}$$

Proof Suppose that for each $s\in \mathcal S$ there exists L as above. By inspection of the canonical form of P provided by the Perron–Frobenius theorem, one deduces that there exists a finite set $\Sigma '$ of possible row vectors $\sigma $ that can arise as limit points for sequences of the form $([P^{m}]_{i,\cdot \,})_{m=1}^\infty $ . Let $\Sigma := \Sigma ' \cup \{\pi \}$ with $\pi $ the unique invariant probability row vector for P. By stochasticity, each $\sigma \in \Sigma $ has nonnegative entries that sum to 1. In this context, by assumption, there exists $L \in {\mathbb N}$ such that

is strictly less than $1$ . Given $\epsilon> 0$ , by inspection of the same canonical form, there exists $m \in {\mathbb N}$ with the following property: for all i, there is $\sigma \in \Sigma $ such that

$$\begin{align*}[P^m]_{i,\,\cdot} < \sigma + \epsilon. \end{align*}$$

Then, taking and $n \geq L$ ,

$$ \begin{align*} {\mathbb P}[a_1^n] &\leq {\mathbb P}[a_1^{L + q(m+L)}] \end{align*} $$

for $q := \max \{k \in {\mathbb N}_0 : n \geq L + k(m+L) \}$ . We introduce the shorthands $\mathcal {R}_0(s) = R_{s_1, a_1} \cdots R_{s_L, a_L}$ ,

$$\begin{align*}\mathcal{R}_k(s) = R_{s_{(k-1)(m+L)+L+1}, a_{(k-1)(m+L)+L+1}} \cdots R_{s_{k(m+L)+L}, a_{k(m+L)+L}} \end{align*}$$

and

$$\begin{align*}\mathcal{R}^{\prime}_k(s) = R_{s_{k(m+L) +1}, a_{k(m+L) +1}} \cdots R_{s_{k(m+L)+L}, a_{k(m+L)+L}} \end{align*}$$

when $1 \leq k \leq q$ . We also identify $s^1_0 \equiv s_L$ , $s^2_0 \equiv s^1_{m+L}$ , $s^3_0 \equiv s^2_{m+L}$ , and so on, and so forth. One then obtains

$$ \begin{align*} &{\mathbb P}[a_1^{L + q(m+L)}]\\ & = \!\! \sum_{\substack{s_1, \dotsc, s_L \\ s_1^{k}, \dotsc, s_{m+L}^k \\ \text{for } 1 \leq k \leq q}} \pi_{s_1} P_{s_1, s_2} \cdots P_{s_{L-1}, s_{L}} \mathcal{R}_0(s) \prod_{k=1}^q P_{s^{k}_{0}, s^k_1}P_{s^{k}_{1}, s^{k}_2} \dotsb P_{s^{k}_{m+L-1}, s^{k}_{m+L}} \mathcal{R}_k(s) \\ & \leq \!\! \sum_{\substack{s_1, \dotsc, s_L \\ s_{m}^{k}, \dotsc, s_{m+L}^k \\ \text{for } 1 \leq k \leq q}} \pi_{s_1} P_{s_1, s_2} \cdots P_{s_{L-1}, s_{L}} \mathcal{R}_0(s) \prod_{k=1}^q (\sigma_{s_m^k}^{(s_0^k)} + \epsilon)P_{s^{k}_{m}, s^{k}_{m+1}} \dotsb P_{s^{k}_{m+L-1}, s^{k}_{m+L}} \mathcal{R}_k'(s) \end{align*} $$

for some appropriate choices of $\sigma ^{(s^{k}_0)} \in \Sigma $ that depend on m and the index $s^{k}_0$ only. Therefore,

$$ \begin{align*} {\mathbb P}[a_1^{L + q(m+L)}] &\leq \delta \cdot (\delta + \epsilon (\#\mathcal{S}))^{q}. \end{align*} $$

By taking $\epsilon>0$ such that $\delta + \epsilon (\#\mathcal {S}) < 1$ and noting that q scales linearly with n, FE holds.

To see the converse implication, suppose that there exists $t\in \mathcal S$ such that there is no L as above. Then, there exists $a \in \Omega $ such that ${\mathbb P}[a_1^n|s_1 = t] = 1$ for all $n \in {\mathbb N}$ . Since

$$ \begin{align*}{\mathbb P}[a_1^n] \geq {\mathbb P}[a_1^n | s_1 = t] \cdot \pi_t = \pi_t\end{align*} $$

for all $n \in {\mathbb N}$ and $\mathrm {e}^{\gamma _{+}n}$ is eventually smaller than $\pi _t> 0$ for all $\gamma _{+} < 0$ , FE fails.

Figure 1: An example that does not satisfy Ad: a direct computation shows that is too unlikely compared to .

One can show that every stationary hidden-Markov measure satisfies the upper bound in ID. But in general – even if P is irreducible – only a weaker form of the lower bound, known as selective lower decoupling, holds (see [Reference Benoist, Cuneo, Jakšić and PilletBCJP21, Section 2] and [Reference Cuneo, Jakšić, Pillet and ShirikyanCJPS19, Section 2]). The fact that selective lower decoupling implies KB but does not imply the condition called Ad in Section 3.4 seems to pose a genuine obstacle. Determining whether the ZM estimation remains generally valid in the class of irreducible, hidden-Markov measures remains – to the best of our knowledge – an important open problem.

In the further specialized case where the elements of R are all in $\{0,1\}$ – this is sometimes called the function-Markov or lumped-Markov case – some conditions for the g-measure property (and thus ID) are discussed in [Reference Chazottes and UgaldeCU03, Reference Verbitskiy, Marcus, Petersen and WeissmanVer11, Reference YooYoo10]. However, it is not difficult to find examples for which none of these known sufficient conditions hold.