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Found 7 papers, 1 papers with code
Estimating the Mixing Coefficients of Geometrically Ergodic Markov Processes
We propose methods to estimate the individual $\beta$-mixing coefficients of a real-valued geometrically ergodic Markov process from a single sample-path $X_0, X_1, \dots, X_n$.
PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series
We introduce PyChEst, a Python package which provides tools for the simultaneous estimation of multiple changepoints in the distribution of piece-wise stationary time series.
Oblivious Data for Fairness with Kernels
We derive a closed-form solution for this relaxed optimization problem and complement the result with a study of the dependencies between the newly generated features and the sensitive ones.
Clustering piecewise stationary processes
The problem of time-series clustering is considered in the case where each data-point is a sample generated by a piecewise stationary ergodic process.
Approximations of the Restless Bandit Problem
The multi-armed restless bandit problem is studied in the case where the pay-off distributions are stationary $\varphi$-mixing.
Locating Changes in Highly Dependent Data with Unknown Number of Change Points
The problem of multiple change point estimation is considered for sequences with unknown number of change points.
Multiple Change Point Estimation in Stationary Ergodic Time Series
Given a heterogeneous time-series sample, the objective is to find points in time (called change points) where the probability distribution generating the data has changed.
