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35
Nonparametric Belief Propagation for SelfCalibration in Sensor Networks
 In Proceedings of the Third International Symposium on Information Processing in Sensor Networks
, 2004
"... Automatic selfcalibration of adhoc sensor networks is a critical need for their use in military or civilian applications. In general, selfcalibration involves the combination of absolute location information (e.g. GPS) with relative calibration information (e.g. time delay or received signal stre ..."
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Cited by 84 (7 self)
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Automatic selfcalibration of adhoc sensor networks is a critical need for their use in military or civilian applications. In general, selfcalibration involves the combination of absolute location information (e.g. GPS) with relative calibration information (e.g. time delay or received signal strength between sensors) over regions of the network. Furthermore, it is generally desirable to distribute the computational burden across the network and minimize the amount of intersensor communication. We demonstrate that the information used for sensor calibration is fundamentally local with regard to the network topology and use this observation to reformulate the problem within a graphical model framework. We then demonstrate the utility of nonparametric belief propagation (NBP), a recent generalization of particle filtering, for both estimating sensor locations and representing location uncertainties. NBP has the advantage that it is easily implemented in a distributed fashion, admits a wide variety of statistical models, and can represent multimodal uncertainty. We illustrate the performance of NBP on several example networks while comparing to a previously published nonlinear least squares method.
Loopy belief propagation: Convergence and effects of message errors
 Journal of Machine Learning Research
, 2005
"... Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, whether due to quantization of the messages or model parameters, from other simplified message or model representations, or ..."
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Cited by 61 (7 self)
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Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, whether due to quantization of the messages or model parameters, from other simplified message or model representations, or from stochastic approximation methods. The introduction of such errors into the BP message computations has the potential to affect the solution obtained adversely. We analyze the effect resulting from message approximation under two particular measures of error, and show bounds on the accumulation of errors in the system. This analysis leads to convergence conditions for traditional BP message passing, and both strict bounds and estimates of the resulting error in systems of approximate BP message passing. 1
Image Sequence Restoration Using Gibbs Distributions
, 1995
"... This thesis addresses a number of issues concerned with the restoration of one type of image sequence, namely archived black and white motion pictures. These are often a valuable historical record, but because of the physical nature of the film they can suffer from a variety of degradations which re ..."
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Cited by 22 (0 self)
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This thesis addresses a number of issues concerned with the restoration of one type of image sequence, namely archived black and white motion pictures. These are often a valuable historical record, but because of the physical nature of the film they can suffer from a variety of degradations which reduce their usefulness. The main visual defects are `dirt and sparkle' due to dust and dirt becoming attached to the film, or abrasion removing the emulsion, and `line scratches' due to the film running against foreign bodies in the camera or projector. For an image
Message errors in belief propagation
 In Advances in Neural Information Processing Systems
, 2004
"... Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, whether from quantization or other simplified message representations or from stochastic approximation methods. Introducing ..."
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Cited by 21 (7 self)
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Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, whether from quantization or other simplified message representations or from stochastic approximation methods. Introducing such errors into the BP message computations has the potential to adversely affect the solution obtained. We analyze this effect with respect to a particular measure of message error, and show bounds on the accumulation of errors in the system. This leads both to convergence conditions and error bounds in traditional and approximate BP message passing. 1
Adaptive Bayesian inference
 In Proc. NIPS
, 2008
"... Motivated by stochastic systems in which observed evidence and conditional dependencies between states of the network change over time, and certain quantities of interest (marginal distributions, likelihood estimates etc.) must be updated, we study the problem of adaptive inference in treestructure ..."
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Cited by 9 (6 self)
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Motivated by stochastic systems in which observed evidence and conditional dependencies between states of the network change over time, and certain quantities of interest (marginal distributions, likelihood estimates etc.) must be updated, we study the problem of adaptive inference in treestructured Bayesian networks. We describe an algorithm for adaptive inference that handles a broad range of changes to the network and is able to maintain marginal distributions, MAP estimates, and data likelihoods in all expected logarithmic time. We give an implementation of our algorithm and provide experiments that show that the algorithm can yield up to two orders of magnitude speedups on answering queries and responding to dynamic changes over the sumproduct algorithm. 1
Markov Connected Component Fields
"... A new class of Gibbsian models with potentials associated to the connected components or homogeneous parts of images is introduced. For these models the neighbourhood of a pixel is not fixed as for Markov random fields, but given by the components which are adjacent to the pixel. The relationship to ..."
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Cited by 7 (2 self)
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A new class of Gibbsian models with potentials associated to the connected components or homogeneous parts of images is introduced. For these models the neighbourhood of a pixel is not fixed as for Markov random fields, but given by the components which are adjacent to the pixel. The relationship to Markov random fields and marked point processes is explored and spatial Markov properties are established. Also extensions to infinite lattices are studied, and statistical inference problems including geostatistical applications and statistical image analysis are discussed. Finally, simulation studies are presented which show that the models may be appropiate for a variety of interesting patterns including images exhibiting intermediate degrees of spatial continuity and images of objects against background.
A graphical model approach for predicting free energies of association for proteinprotein interactions under backbone . . .
, 2008
"... ..."
QuermassInteraction Processes: Conditions for Stability
"... We consider a class of random point and germgrain processes, obtained using a rather natural weighting procedure. Given a Poisson point process, on each point one places a grain, a (possibly random) compact convex set. Let \Xi be the union of all grains. One can now construct new processes whose de ..."
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Cited by 5 (1 self)
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We consider a class of random point and germgrain processes, obtained using a rather natural weighting procedure. Given a Poisson point process, on each point one places a grain, a (possibly random) compact convex set. Let \Xi be the union of all grains. One can now construct new processes whose density is derived from an exponential of a linear combination of quermass functionals of \Xi. If only the area functional is used, then the areainteraction point process is recovered. New point processes arise if we include the perimeter length functional, or the Euler functional (number of components minus number of holes). The main question addressed by the paper is that of when the resulting point process is welldefined: geometric arguments are used to establish conditions for the point process to be stable in the sense of Ruelle. Key words: areainteraction point process, Boolean model, germgrain model, Markov point process, Minkowski functional, quermass integral, semiMarkov random ...