If a stoch. process Xt has independent and weak stationary increments. var(Xt) = σ^2 for all t, prove that Cov(xt,xs) = min(t,s)σ^2(adsbygoogle = window.adsbygoogle || []).push({});

I'm not sure how to do this. I tried using the definition of covariance but that doesn't really lead me anywhere. If it's stationary that means the distribution doesn't change as time changes. I was thinking of setting s = t+k and showing the covariance being min(t, t+k)var(Xt) but I don't know how to get to there.

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# Stochastic process proof

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