Conditionally Resampled Sliding-Window Count Kernels: Spectral-Gap Bounds and Poincar'e Inequalities

We study the conditionally resampled sliding-window count kernel associated with the empirical counts of length- n windows from a stationary finite-state reversible Markov chain. Although the resulting count process is generally not Markov, its stationary one-step conditional law defines a genuine Markov kernel. For every fixed strictly positive reversible kernel P on a finite state space, we present a Poincar'e inequality for the induced count kernel n of length n. In other words, we derive the lower bound of the spectral gap (n) of n as (n) c(P) over n, where c(P)>0 depends only on P. The proof combines a martingale oscillation inequality for the stationary path law with a direct comparison of coordinate oscillations to the Dirichlet form of the count kernel. A linear statistic of the count vector gives the matching O(1/n) upper bound, so for every fixed strictly positive reversible P one has (n)= P(1/n). The resulting count-space Poincar'e inequality yields a local-to-global variance bound for finite-window count statistics and, together with a general matrix-concentration principle, operator-norm concentration for matrix-valued empirical averages.