314 research outputs found
Discrete analogue computing with rotor-routers
Rotor-routing is a procedure for routing tokens through a network that can
implement certain kinds of computation. These computations are inherently
asynchronous (the order in which tokens are routed makes no difference) and
distributed (information is spread throughout the system). It is also possible
to efficiently check that a computation has been carried out correctly in less
time than the computation itself required, provided one has a certificate that
can itself be computed by the rotor-router network. Rotor-router networks can
be viewed as both discrete analogues of continuous linear systems and
deterministic analogues of stochastic processes.Comment: To appear in Chaos Special Focus Issue on Intrinsic and Designed
Computatio
Orientation-Constrained Rectangular Layouts
We construct partitions of rectangles into smaller rectangles from an input
consisting of a planar dual graph of the layout together with restrictions on
the orientations of edges and junctions of the layout. Such an
orientation-constrained layout, if it exists, may be constructed in polynomial
time, and all orientation-constrained layouts may be listed in polynomial time
per layout.Comment: To appear at Algorithms and Data Structures Symposium, Banff, Canada,
August 2009. 12 pages, 5 figure
Algebraic arctic curves in the domain-wall six-vertex model
The arctic curve, i.e. the spatial curve separating ordered (or `frozen') and
disordered (or `temperate) regions, of the six-vertex model with domain wall
boundary conditions is discussed for the root-of-unity vertex weights. In these
cases the curve is described by algebraic equations which can be worked out
explicitly from the parametric solution for this curve. Some interesting
examples are discussed in detail. The upper bound on the maximal degree of the
equation in a generic root-of-unity case is obtained.Comment: 15 pages, no figures; v2: metadata correcte
The arctic curve of the domain-wall six-vertex model in its anti-ferroelectric regime
An explicit expression for the spatial curve separating the region of
ferroelectric order (`frozen' zone) from the disordered one (`temperate' zone)
in the six-vertex model with domain wall boundary conditions in its
anti-ferroelectric regime is obtained.Comment: 12 pages, 1 figur
Functional relations for the six vertex model with domain wall boundary conditions
In this work we demonstrate that the Yang-Baxter algebra can also be employed
in order to derive a functional relation for the partition function of the six
vertex model with domain wall boundary conditions. The homogeneous limit is
studied for small lattices and the properties determining the partition
function are also discussed.Comment: 19 pages, v2: typos corrected, new section and appendix added. v3:
minor corrections, to appear in J. Stat. Mech
Exact sampling from non-attractive distributions using summary states
Propp and Wilson's method of coupling from the past allows one to efficiently
generate exact samples from attractive statistical distributions (e.g., the
ferromagnetic Ising model). This method may be generalized to non-attractive
distributions by the use of summary states, as first described by Huber. Using
this method, we present exact samples from a frustrated antiferromagnetic
triangular Ising model and the antiferromagnetic q=3 Potts model. We discuss
the advantages and limitations of the method of summary states for practical
sampling, paying particular attention to the slowing down of the algorithm at
low temperature. In particular, we show that such a slowing down can occur in
the absence of a physical phase transition.Comment: 5 pages, 6 EPS figures, REVTeX; additional information at
http://wol.ra.phy.cam.ac.uk/mackay/exac
Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks
Undirected graphical models are widely used in statistics, physics and
machine vision. However Bayesian parameter estimation for undirected models is
extremely challenging, since evaluation of the posterior typically involves the
calculation of an intractable normalising constant. This problem has received
much attention, but very little of this has focussed on the important practical
case where the data consists of noisy or incomplete observations of the
underlying hidden structure. This paper specifically addresses this problem,
comparing two alternative methodologies. In the first of these approaches
particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently
explore the parameter space, combined with the exchange algorithm (Murray et
al., 2006) for avoiding the calculation of the intractable normalising constant
(a proof showing that this combination targets the correct distribution in
found in a supplementary appendix online). This approach is compared with
approximate Bayesian computation (Pritchard et al., 1999). Applications to
estimating the parameters of Ising models and exponential random graphs from
noisy data are presented. Each algorithm used in the paper targets an
approximation to the true posterior due to the use of MCMC to simulate from the
latent graphical model, in lieu of being able to do this exactly in general.
The supplementary appendix also describes the nature of the resulting
approximation.Comment: 26 pages, 2 figures, accepted in Journal of Computational and
Graphical Statistics (http://www.amstat.org/publications/jcgs.cfm
Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels
Monte Carlo algorithms often aim to draw from a distribution by
simulating a Markov chain with transition kernel such that is
invariant under . However, there are many situations for which it is
impractical or impossible to draw from the transition kernel . For instance,
this is the case with massive datasets, where is it prohibitively expensive to
calculate the likelihood and is also the case for intractable likelihood models
arising from, for example, Gibbs random fields, such as those found in spatial
statistics and network analysis. A natural approach in these cases is to
replace by an approximation . Using theory from the stability of
Markov chains we explore a variety of situations where it is possible to
quantify how 'close' the chain given by the transition kernel is to
the chain given by . We apply these results to several examples from spatial
statistics and network analysis.Comment: This version: results extended to non-uniformly ergodic Markov chain
On FPL configurations with four sets of nested arches
The problem of counting the number of Fully Packed Loop (FPL) configurations
with four sets of a,b,c,d nested arches is addressed. It is shown that it may
be expressed as the problem of enumeration of tilings of a domain of the
triangular lattice with a conic singularity. After reexpression in terms of
non-intersecting lines, the Lindstr\"om-Gessel-Viennot theorem leads to a
formula as a sum of determinants. This is made quite explicit when
min(a,b,c,d)=1 or 2. We also find a compact determinant formula which generates
the numbers of configurations with b=d.Comment: 22 pages, TeX, 16 figures; a new formula for a generating function
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