Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stationary Geometric Graphical Model Selection   We consider the problem of model selection in Gaussian Markov fields in the
sample deficient scenario. In many practically important cases, the underlying
networks are embedded into Euclidean spaces. Using the natural geometric
structure, we introduce the notion of spatially stationary distributions over
geometric graphs. This directly generalizes the notion of stationary time
series to the multidimensional setting lacking time axis. We show that the idea
of spatial stationarity leads to a dramatic decrease in the sample complexity
of the model selection compared to abstract graphs with the same level of
sparsity. For geometric graphs on randomly spread vertices and edges of bounded
length, we develop tight information-theoretic bounds on sample complexity and
show that a finite number of independent samples is sufficient for a consistent
recovery. Finally, we develop an efficient technique capable of reliably and
consistently reconstructing graphs with a bounded number of measurements.

29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph