On the Central Limit Question Under Absolute Regularity

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Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning (2019, background): “his requires sucient ergodicity so that non-adjacent folds are near-independent. We express this condition in terms of a ˚-mixing condition. Note that ˚-mixing is stronger than ˆ-mixing and -mixing (Bradley, 1985). Theorem 10 (Eciency of ˆ^ DRL(M 3) with cross-time-tting). Assume (9a) w^(T j);^q(T j”
On the Markov chain central limit theorem (2004, methodology): “However, some constructive methods are given in the following brief discussion; the interested reader should consult Jarner and Roberts (2002) and Meyn and Tweedie (1993) for a more complete introduction to these methods.”
Stationary Stochastic Processes (2011, unknown): “Bradley (1986) establishes the inequalities 2αn ≤ βn ≤ φn 4αn ≤ ρn ≤ 2 √ φn and also states that alpha-mixing implies mixing.”