Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient simulation of high dimensional Gaussian vectors   We describe a Markov chain Monte Carlo method to approximately simulate a
centered d-dimensional Gaussian vector X with given covariance matrix. The
standard Monte Carlo method is based on the Cholesky decomposition, which takes
cubic time and has quadratic storage cost in d. In contrast, the storage cost
of our algorithm is linear in d. We give a bound on the quadractic Wasserstein
distance between the distribution of our sample and the target distribution.
Our method can be used to estimate the expectation of h(X), where h is a
real-valued function of d variables. Under certain conditions, we show that the
mean square error of our method is inversely proportional to its running time.
We also prove that, under suitable conditions, our method is faster than the
standard Monte Carlo method by a factor nearly proportional to d. A numerical
example is given.

24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics