Let $X=\{0,1\}^{\mathbb{N}}$. For simplicity I consider measures on $X$ only.

A measure $\mu$ is **quasi-Bernoulli** if there is a constant $C\ge 1$ such that for any finite sequences $i,j$,
$$
C^{-1} \mu[ij] \le \mu[i]\mu[j] \le C\mu[ij].
$$

(Here as usual $ij$ is the juxtaposition of $i$ and $j$ and $[k]$ is the cylinder of all infinite sequences starting with $k$.)

Let $f:X\to \mathbb{R}$ be continuous. The measure $\mu$ is a **Gibbs measure** with potential $f$ if there are $C>0$ and $P\in\mathbb{R}$, such that for every infinite sequence $i_1 i_2\ldots$ and all natural $n$,
$$
C^{-1} \le \frac{\mu[i_1\ldots i_n]}{\exp(-nP+f(i)+f(\sigma i)+\cdots+f(\sigma^{n-1}i))} \le C,
$$
where $\sigma$ is the left shift.

Of course, there are other definitions of Gibbs measure, but they all agree if the potential $f$ is Hölder. In this case, it follows readily that a Gibbs measure is quasi-Bernoulli.

If a measure is quasi-Bernoulli, there is an equivalent measure (mutually absolutely continuous with bounded densities) which is invariant and ergodic under the shift.

**Question**: Are all quasi-Bernoulli measures Gibbs? (for some continuous potential, not necessarily Hölder). If not, what is a counterexample?

**Motivation**: Gibbs measures (with Hölder potentials) enjoy many nice statistical properties. Sometimes I have a measure that is quasi-Bernoulli or satisfies some similar but weaker property. I would like to understand if and to what extent good statistical properties continue to hold in that setting.