Are there any metrics that approximate the order of a markov process?

I’m working with Approximate Entropy for my thesis. It basically is a way to approximate the difference in conditional distributions of a markov process at different segments in time. The idea is that the larger the change in distributions, the more complex a system (random time series’ distribution wont change that much, neither would a simple time series). I feel like this metric doesn’t work well across different time scales. Now there are other variants of this metric that try to handle this, but I’m curious if the underlying distribution is even remotely relevant. If the goal is to measure complexity, wouldn’t knowing the order of the markov process be the best metric? If your process depends on something that happened 100 steps ago, then it’s more complex and naturally lead to a bigger change in probability distributions between step 99 and 100 (compared to one that just depends on one step). It would also account for all time scales, because the steps can occur at any time interval. submitted by /u/pretysmitty [link] [comments]

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Nevin Manimala

Nevin Manimala is interested in blogging and finding new blogs

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