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Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory.

2 votes
1 answer
249 views

Fell topology versus vague topology for representing random sets

I'm trying to better understand the consequences of representing a random set as a Random element in the space of locally finite closed sets under the Borel sigma algebra generated by the Fell topo …
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  • 321
3 votes
2 answers
212 views

Moving a result from the unconditional to the conditional

I'm generally wary when lifting a result stated unconditionally to a situation where I'm conditioning on a random variable. Consider the following classical result in weak convergence: Theorem. Let …
D.R.'s user avatar
  • 321
3 votes
0 answers
831 views

Total variation, Wasserstein, and Prokhorov metrics on countably infinite discrete spaces

Total variation, Wasserstein, and Prokhorov generate the same topology on the space of probability measures on a finite and discrete space. I'm curious about a countably infinite space. When do th …
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  • 321
1 vote

Probability theory without deductive closure

If you model a human brain as a stochastic reasoning device, you can posit uncertainty as to whether the mechanism of the brain has, at some point in time, reached some logical conclusion and stored i …
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  • 321
1 vote
0 answers
87 views

Convergence of an rcll process along a random subsequence

I have a process $X_s$, for $s \ge 0$, taking values in a Polish space $T$ with an rcll version where I have shown, for every nonrandom increasing sequence $s_n$, that $X_{s_n} \to c$ in probability, …
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  • 321