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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.

8 votes

Normal distribution with positive SEMI-definite covariance matrix

As the commenters have already mentioned, there isn't a probability density function in the case where the covariance matrix is singular. Rather, you have a distribution that lives on a lower dimensi …
Brian Borchers's user avatar
5 votes

Characterising semi-definite positiveness on vectors with non-negative entries

Your cone $C$ is the cone of copositive matrices. The dual of C is the cone of compeltely positive matrices. See e.g. http://mathworld.wolfram.com/CopositiveMatrix.html
Brian Borchers's user avatar
4 votes

Random versions of deterministic problems

See The Probabilistic Method by Noga Alon and Joel H. Spencer.
2 votes

On Random Vectors and Eigenvectors of Symmetric Matrices

In this case, it's easy enough to compute the probability exactly in terms of the incomplete beta function. Let $v$ be the fixed unit vector, and $r$ be the random unit vector, uniformly distributed …
Brian Borchers's user avatar
1 vote

Multiplying two probability distributions represented by particles

You seem a bit confused about multiplication and convolution. Do you need the convolution of the two pdf's (this would give you the pdf of the sum of the random variables), or the product of the pdf' …
Brian Borchers's user avatar