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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
What are some useful invariants for distinguishing between random graph models?
It really depends on your applications/goals, and the graphs you consider.
Do you want to compare graph sequences or graphs of a fixed size?
First let's consider sequences.
The problem with propert …
1
vote
Zero-one law in binomial random graph model $G(n,p)$
I'm not an expert on that specific question, and it's certainly the most difficult regime of $\mathcal{G}(n,p)$ to understand, so I'll just show the path.
First, the question can be generalized as $p …
1
vote
Learn a distribution from distributions on samples
The exact results really depend on all the particularities of your model (how do you allow errors, can you ask for specific samples, the nature of the noise, etc, etc). It may be a feasible task or no …