1 code implementation • 8 Apr 2024 • Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj
Operating in the general setting in which the size of the state space may be much larger than the length $n$ of the trajectory, we develop a linear-runtime estimator called \emph{Windowed Good--Turing} (\textsc{WingIt}) and show that its risk decays as $\widetilde{\mathcal{O}}(\mathsf{T_{mix}}/n)$, where $\mathsf{T_{mix}}$ denotes the mixing time of the chain in total variation distance.
no code implementations • 3 Feb 2021 • Prafulla Chandra, Andrew Thangaraj, Nived Rajaraman
In this work, we study convergence of the GT estimator for missing stationary mass (i. e., total stationary probability of missing symbols) of Markov samples on an alphabet $\mathcal{X}$ with stationary distribution $[\pi_x:x \in \mathcal{X}]$ and transition probability matrix (t. p. m.)
