Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Simple Algorithm for Scalable Monte Carlo Inference   The methods of statistical physics are widely used for modelling complex
networks. Building on the recently proposed Equilibrium Expectation approach,
we derive a simple and efficient algorithm for maximum likelihood estimation
(MLE) of parameters of exponential family distributions - a family of
statistical models, that includes Ising model, Markov Random Field and
Exponential Random Graph models. Computational experiments and analysis of
empirical data demonstrate that the algorithm increases by orders of magnitude
the size of network data amenable to Monte Carlo based inference. We report
results suggesting that the applicability of the algorithm may readily be
extended to the analysis of large samples of dependent observations commonly
found in biology, sociology, astrophysics, and ecology.

37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
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
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex