67 research outputs found
The Role of Normalization in the Belief Propagation Algorithm
An important part of problems in statistical physics and computer science can
be expressed as the computation of marginal probabilities over a Markov Random
Field. The belief propagation algorithm, which is an exact procedure to compute
these marginals when the underlying graph is a tree, has gained its popularity
as an efficient way to approximate them in the more general case. In this
paper, we focus on an aspect of the algorithm that did not get that much
attention in the literature, which is the effect of the normalization of the
messages. We show in particular that, for a large class of normalization
strategies, it is possible to focus only on belief convergence. Following this,
we express the necessary and sufficient conditions for local stability of a
fixed point in terms of the graph structure and the beliefs values at the fixed
point. We also explicit some connexion between the normalization constants and
the underlying Bethe Free Energy
Local stability of Belief Propagation algorithm with multiple fixed points
A number of problems in statistical physics and computer science can be
expressed as the computation of marginal probabilities over a Markov random
field. Belief propagation, an iterative message-passing algorithm, computes
exactly such marginals when the underlying graph is a tree. But it has gained
its popularity as an efficient way to approximate them in the more general
case, even if it can exhibits multiple fixed points and is not guaranteed to
converge. In this paper, we express a new sufficient condition for local
stability of a belief propagation fixed point in terms of the graph structure
and the beliefs values at the fixed point. This gives credence to the usual
understanding that Belief Propagation performs better on sparse graphs.Comment: arXiv admin note: substantial text overlap with arXiv:1101.417
A queueing theory approach for a multi-speed exclusion process.
10 pages, 6 figuresInternational audienceWe consider a one-dimensional stochastic reaction-diffusion generalizing the totally asymmetric simple exclusion process, and aiming at describing single lane roads with vehicles that can change speed. To each particle is associated a jump rate, and the particular dynamics that we choose (based on 3-sites patterns) ensures that clusters of occupied sites are of uniform jump rate. When this model is set on a circle or an infinite line, classical arguments allow to map it to a linear network of queues (a zero-range process in theoretical physics parlance) with exponential service times, but with a twist: the service rate remains constant during a busy period, but can change at renewal events. We use the tools of queueing theory to compute the fundamental diagram of the traffic, and show the effects of a condensation mechanism
Pairwise MRF Calibration by Perturbation of the Bethe Reference Point
We investigate different ways of generating approximate solutions to the
pairwise Markov random field (MRF) selection problem. We focus mainly on the
inverse Ising problem, but discuss also the somewhat related inverse Gaussian
problem because both types of MRF are suitable for inference tasks with the
belief propagation algorithm (BP) under certain conditions. Our approach
consists in to take a Bethe mean-field solution obtained with a maximum
spanning tree (MST) of pairwise mutual information, referred to as the
\emph{Bethe reference point}, for further perturbation procedures. We consider
three different ways following this idea: in the first one, we select and
calibrate iteratively the optimal links to be added starting from the Bethe
reference point; the second one is based on the observation that the natural
gradient can be computed analytically at the Bethe point; in the third one,
assuming no local field and using low temperature expansion we develop a dual
loop joint model based on a well chosen fundamental cycle basis. We indeed
identify a subclass of planar models, which we refer to as \emph{Bethe-dual
graph models}, having possibly many loops, but characterized by a singly
connected dual factor graph, for which the partition function and the linear
response can be computed exactly in respectively O(N) and operations,
thanks to a dual weight propagation (DWP) message passing procedure that we set
up. When restricted to this subclass of models, the inverse Ising problem being
convex, becomes tractable at any temperature. Experimental tests on various
datasets with refined or regularization procedures indicate that
these approaches may be competitive and useful alternatives to existing ones.Comment: 54 pages, 8 figure. section 5 and refs added in V
Global On-line Optimization for Charging Station Allocation
International audienceThe goal of the Mobility2.0 project was to provide full electric vehicles users with a set of tools that reduce range anxiety and favor a partial modal shift towards public transportation. As part of this work, we have designed a so-called global optimization criterion for selecting charging stations where change of mode can occur. The idea is to minimize the mean quadratic travel time of all users, in a way that can be used for on-line allocation with good performance. We show through simulation that this leads to a sizable improvement with respect to a "greedy" station selection
Latent binary MRF for online reconstruction of large scale systems
International audienceWe present a novel method for online inference of real-valued quantities on a large network from very sparse measurements. The target application is a large scale system, like e.g. a traffic network, where a small varying subset of the variables is observed, and predictions about the other variables have to be continuously updated. A key feature of our approach is the modeling of dependencies between the original variables through a latent binary Markov random field. This greatly simplifies both the model selection and its subsequent use. We introduce the mirror belief propagation algorithm, that performs fast inference in such a setting. The offline model estimation relies only on pairwise historical data and its complexity is linear w.r.t. the dataset size. Our method makes no assumptions about the joint and marginal distributions of the variables but is primarily designed with multimodal joint distributions in mind. Numerical experiments demonstrate both the applicability and scalability of the method in practice
Birth and death processes on certain random trees: Classification and stationary laws
The main substance of the paper concerns the growth rate and the
classification (ergodicity, transience) of a family of random trees. In the
basic model, new edges appear according to a Poisson process of parameter
and leaves can be deleted at a rate . The main results lay the
stress on the famous number . A complete classification of the process is
given in terms of the intensity factor : it is ergodic if
, and transient if . There is a phase transition
phenomenon: the usual region of null recurrence (in the parameter space) here
does not exist. This fact is rare for countable Markov chains with
exponentially distributed jumps. Some basic stationary laws are computed, e.g.
the number of vertices and the height. Various bounds, limit laws and
ergodic-like theorems are obtained, both for the transient and ergodic regimes.
In particular, when the system is transient, the height of the tree grows
linearly as the time , at a rate which is explicitly computed. Some
of the results are extended to the so-called multiclass model
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