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madness
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Hi all. I've been getting up to speed with Gaussian processes (https://en.wikipedia.org/wiki/Gaussian_process), and was interested to know what properties a Gaussian process must satisfy for it to also be a Markov process (https://en.wikipedia.org/wiki/Markov_process).
Briefly, a Gaussian process is an ordered collection of Gaussian random variables (e.g., a time series) which can be characterised by the covariance function between variables representing different time points t and t'. A Markov process is one in which the state at some time t > t' can be predicted just as well by the state at time t' as it could be including additional information from times t < t'.
According to the wiki page on a Gauss-Markov process (https://en.wikipedia.org/wiki/Gauss–Markov_process), a condition a Gaussian process must satisfy in order to be a Markov process is that it have an exponential covariance function [tex]\sigma^2 e^{-\beta |t - t'|}[/tex]. Does anyone have any idea why this should be the case? Why not [tex]\sigma^2 e^{-\beta |t - t'|^2}[/tex] for example?
Thanks!
Briefly, a Gaussian process is an ordered collection of Gaussian random variables (e.g., a time series) which can be characterised by the covariance function between variables representing different time points t and t'. A Markov process is one in which the state at some time t > t' can be predicted just as well by the state at time t' as it could be including additional information from times t < t'.
According to the wiki page on a Gauss-Markov process (https://en.wikipedia.org/wiki/Gauss–Markov_process), a condition a Gaussian process must satisfy in order to be a Markov process is that it have an exponential covariance function [tex]\sigma^2 e^{-\beta |t - t'|}[/tex]. Does anyone have any idea why this should be the case? Why not [tex]\sigma^2 e^{-\beta |t - t'|^2}[/tex] for example?
Thanks!