Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Randomness Recycler: A new technique for perfect sampling   For many probability distributions of interest, it is quite difficult to
obtain samples efficiently. Often, Markov chains are employed to obtain
approximately random samples from these distributions. The primary drawback to
traditional Markov chain methods is that the mixing time of the chain is
usually unknown, which makes it impossible to determine how close the output
samples are to having the target distribution. Here we present a new protocol,
the randomness recycler (RR), that overcomes this difficulty. Unlike classical
Markov chain approaches, an RR-based algorithm creates samples drawn exactly
from the desired distribution. Other perfect sampling methods such as coupling
from the past use existing Markov chains, but RR does not use the traditional
Markov chain at all. While by no means universally useful, RR does apply to a
wide variety of problems. In restricted instances of certain problems, it gives
the first expected linear time algorithms for generating samples. Here we apply
RR to self-organizing lists, the Ising model, random independent sets, random
colorings, and the random cluster model.

35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
1
The categories of this paper are: cs
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ