2015•Unpublished venueRequires access

Effects of downsampling on statistics of discrete-time semi-Markov processes

Phuoc Vu, Shuangqing Wei, Benjamin Carroll

Open publisher page 2 citations

Abstract

In this paper, we first present a novel approach to finding the statistics of the downsampled sequence of a discrete time semi-Markov process in terms of the sojourn time distributions and states transition matrix of the resulting process. Moreover, we further show that the statistics of the original semi-Markov process cannot be uniquely determined given the downsampled sequence. This suggests a singularity issue resulting from the downsampling regardless of the bandwidth of the original process. Numerical results based on derived theoretical investigation have been further verified using simulations.

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What this paper is about

In this paper, we first present a novel approach to finding the statistics of the downsampled sequence of a discrete time semi-Markov process in terms of the sojourn time distributions and states transition matrix of the resulting process. Moreover, we further show that the statistics of the original semi-Markov process cannot be uniquely determined given the downsampled sequence. This suggests a singularity issue resulting from the downsampling regardless of the bandwidth of the original process. Numerical results based on derived theoretical investigation have been further verified using simulations.

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Available abstract

In this paper, we first present a novel approach to finding the statistics of the downsampled sequence of a discrete time semi-Markov process in terms of the sojourn time distributions and states transition matrix of the resulting process. Moreover, we further show that the statistics of the original semi-Markov process cannot be uniquely determined given the downsampled sequence. This suggests a singularity issue resulting from the downsampling regardless of the bandwidth of the original process. Numerical results based on derived theoretical investigation have been further verified using simulations.

Key concepts: Markov process, Markov chain, Upsampling, Discrete time and continuous time, Sequence (biology), Stochastic process, Applied mathematics, Markov kernel

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