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Can any point process be thinned into a homogenous Poisson point process?
Modified to (an approximation of) a homogenous Poisson point process, to recognize the counter-example that a Mátern hard-core point process can never be thinned into a homogenous Poisson point process.
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Can any point process be thinned into a homogenous Poisson point process?
@JamesMartin Yes, that's a very good point, and possibly a fatal one. The tickle in my brain is as follows: Let $X$ be (the realisation of) a Mátern hard-core point process. Perhaps if $Y$ is a very "aggressive" thinning of $X$ then it will be a good approximation to a homogenous Poisson process, becoming perfect in the limit? So yes, there are no points within distance $r$, but on the big scale the $r$ looks like it is tending to zero?
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Smoothness and curvature of geodesics in a length space
The question that was posed has an intuitive interpretation, "Suppose that $p(x)$ represents the density of the material at any point $x$. If changes in density are 'nice' throughout $X$, is it true that shortest paths are smooth and of bounded curvature?" The intuition is "yes" if $p(x)$ is smooth.
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Sum of a random number of identically distributed but dependent random variables?
Calculation of $\sigma^2_Q$ is given as an approximation, not equality.
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Sum of a random number of identically distributed but dependent random variables?
My secondary conjecture turned out to be wrong. I think I've found a proof as described in this update.
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Using upper bound information in graph search
Expanded answer to clarify what is known and not known in the statement of the problem.
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Sum of a random number of identically distributed but dependent random variables?
Link to MO question on new conjecture.
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Publishing an elementary proof of a less-general and less-useful version of a classic result?
Left out the variable of integration in calculation \zeta.
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Sum of a random number of identically distributed but dependent random variables?
$Q$ is asymptotically normal as $ \tau \rightarrow \infty $
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Publishing an elementary proof of a less-general and less-useful version of a classic result?
'stochastic process' in place of 'continuous time Markov process'. The original was a typo, since I was studying generic processes.
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