Results 1  10
of
15
Bayesian indoor positioning systems
 In Infocom
, 2005
"... Abstract — In this paper, we introduce a new approach to location estimation where, instead of locating a single client, we simultaneously locate a set of wireless clients. We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model ach ..."
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Cited by 67 (13 self)
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Abstract — In this paper, we introduce a new approach to location estimation where, instead of locating a single client, we simultaneously locate a set of wireless clients. We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model achieves accuracy that is similar to other published models and algorithms. By harnessing prior knowledge, our model eliminates the requirement for training data as compared with existing approaches, thereby introducing the notion of a fully adaptive zero profiling approach to location estimation. Index Terms — Experimentation with real networks/Testbed, Statistics, WLAN, localization,
Probabilistic models for relational data
, 2004
"... We introduce a graphical language for relational data called the probabilistic entityrelationship (PER) model. The model is an extension of the entityrelationship model, a common model for the abstract representation of database structure. We concentrate on the directed version of this model—the di ..."
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Cited by 46 (0 self)
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We introduce a graphical language for relational data called the probabilistic entityrelationship (PER) model. The model is an extension of the entityrelationship model, a common model for the abstract representation of database structure. We concentrate on the directed version of this model—the directed acyclic probabilistic entityrelationship (DAPER) model. The DAPER model is closely related to the plate model and the probabilistic relational model (PRM), existing models for relational data. The DAPER model is more expressive than either existing model, and also helps to demonstrate their similarity. In addition to describing the new language, we discuss important facets of modeling relational data, including the use of restricted relationships, self relationships, and probabilistic relationships. Many examples are provided.
Spatial Poisson Regression for Health and Exposure Data Measured at Disparate Resolutions
 Journal of the American Statistical Association
, 2000
"... This paper presents a spatial regression analysis of the effect of traffic pollution on respiratory disorders in children. The analysis features data measured at disparate, nonnested scales, including spatially varying covariates, latent spatially varying risk factors, and casespecific individual ..."
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Cited by 38 (9 self)
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This paper presents a spatial regression analysis of the effect of traffic pollution on respiratory disorders in children. The analysis features data measured at disparate, nonnested scales, including spatially varying covariates, latent spatially varying risk factors, and casespecific individual attributes
Graphical Models for Genetic Analyses
 STATISTTICAL SCIENCE
, 2003
"... This paper introduces graphical models as a natural environment in which to formulate and solve problems in genetics and related areas. Particular emphasis is given to the relationships among various local computation algorithms which have been developed within the hitherto mostly separate areas o ..."
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Cited by 28 (0 self)
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This paper introduces graphical models as a natural environment in which to formulate and solve problems in genetics and related areas. Particular emphasis is given to the relationships among various local computation algorithms which have been developed within the hitherto mostly separate areas of graphical models and genetics. The potential of graphical models is explored and illustrated through a number of example applications where the genetic element is substantial or dominating.
Some Modern Applications of Graphical Models
 Highly Structured Stochastic Systems
, 2001
"... This article reviews a number of modern applications of graphical models from diverse areas, such as decision support systems, telecommunication, and machine learning. ..."
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Cited by 3 (0 self)
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This article reviews a number of modern applications of graphical models from diverse areas, such as decision support systems, telecommunication, and machine learning.
Reducing the Computational Cost of Bayesian Indoor Positioning Systems
, 2006
"... In this work we show how to reduce the computational cost of using Bayesian networks for localization. We investigate a range of Monte Carlo sampling strategies, including Gibbs and Metropolis. We found that for our Gibbs samplers, most of the time is spent in slice sampling. Moreover, our results ..."
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Cited by 2 (2 self)
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In this work we show how to reduce the computational cost of using Bayesian networks for localization. We investigate a range of Monte Carlo sampling strategies, including Gibbs and Metropolis. We found that for our Gibbs samplers, most of the time is spent in slice sampling. Moreover, our results show that although uniform sampling over the entire domain suffers occasional rejections, it has a much lower overall computational cost than approaches that carefully avoid rejections. The key reason for this efficiency is the flatness of the full conditionals in our localization networks. Our sampling technique is also attractive because it does not require extensive tuning to achieve good performance, unlike the Metropolis samplers. We demonstrate that our whole domain sampling technique converges accurately with low latency. On commodity hardware our sampler localizes up to 10 points in less than half a second, which is over 10 times faster than a common generalpurpose Bayesian sampler. Our sampler also scales well, localizing 51 objects with no location information in the training set in less than 6 seconds. Finally, we present an analytic model that describes the number of evaluations per variable using slice sampling. The model allows us to analytically determine how flat a distribution should be so that whole domain sampling is computationally more efficient when compared to other methods.
Bayesian Data Analysis for Data Mining
 In Handbook of Data Mining
, 2002
"... Introduction The Bayesian approach to data analysis computes conditional probability distribu tions of quantities of interest (such as future observables) given the observed data. Bayesian analyses usually begin with a .full probability model  a joint probability dis tribution for all the observ ..."
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Cited by 1 (0 self)
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Introduction The Bayesian approach to data analysis computes conditional probability distribu tions of quantities of interest (such as future observables) given the observed data. Bayesian analyses usually begin with a .full probability model  a joint probability dis tribution for all the observable and unobservable quantities under study  and then use Bayes' theorem (Bayes, 1763) to compute the requisite conditional probability distributions (called poster'Joy distributions). The theorem itself is innocuous enough. In its simplest form, if Q denotes a quantity of interest and D denotes data, the theorem states: P(ql D) P(;lq) X P(q)/P(). This theorem prescribes the basis for statistical learning in the probabilistic frame work. With p(Q) regarded as a probabilistic statement of prior knowledge about Q before obtaining the data D, p(QI D) becomes a revised probabilistic statement of our knowledge about Q in the light of the data (Bernardo and Smith, 1994, p.2). The marginal lik
Strategies for Inference Robustness in Complex Modelling: An Application to Longitudinal Performance Measures.
, 1999
"... Advances in computation mean it is now possible to fit a wide range of complex models, but selecting a model on which to base reported inferences is a difficult problem. Following an early suggestion of Box and Tiao, it seems reasonable to seek `inference robustness' in reported models, so that a ..."
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Cited by 1 (0 self)
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Advances in computation mean it is now possible to fit a wide range of complex models, but selecting a model on which to base reported inferences is a difficult problem. Following an early suggestion of Box and Tiao, it seems reasonable to seek `inference robustness' in reported models, so that alternative assumptions that are reasonably well supported would not lead to substantially different conclusions. We propose a fourstage modelling strategy in which we: iteratively assess and elaborate an initial model, measure the support for each of the resulting family of models, assess the influence of adopting alternative models on the conclusions of primary interest, and identify whether an approximate model can be reported. These stages are semiformal, in that they are embedded in a decisiontheoretic framework but require substantive input for any specific application. The ideas are illustrated on a dataset comprising the success rates of 46 invitro fertilisation clinics over three years. The analysis supports a model that assumes 43 of the 46 clinics have odds on success that are evolving at a constant proportional rate (i.e. linear on a logit scale), while three clinics are outliers in the sense of showing nonlinear trends. For the 43 `linear' clinics, the intercepts and gradients can be assumed to follow a bivariate normal distribution except for one outlying intercept: the odds on success are significantly increasing for four clinics and significantly decreasing for three. This model displays considerable inference robustness and, although its conclusions could be approximated by other lesssupported models, these would not be any more parsimonious. Technical issues include fitting mixture models of alternative hierarchical longitudinal models, t...
Reasoning From NonStationarity
 Physica A: Statistical Mechanics and its Applications
, 2002
"... Complex realworld (biological) systems often exhibit intrinsically nonstationary behaviour of their temporal characteristics. We discuss local measures of scaling which can capture and reveal changes in a system's behaviour. ..."
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Cited by 1 (1 self)
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Complex realworld (biological) systems often exhibit intrinsically nonstationary behaviour of their temporal characteristics. We discuss local measures of scaling which can capture and reveal changes in a system's behaviour.
Familial Tendency to Fetal Loss Analyzed with Bayesian Graphical Models by Gibbs Sampling
 Statistics in Medicine
, 1999
"... This paper presents several models for investigating whether the HLA allogenotypes DR1/Br, DR3 and DR10 are genetic markers for a predisposition of experiencing unexplained recurrent fetal losses. A total of 199 women from 113 families answered questionnaires concerning their pregnancies and 145 of ..."
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Cited by 1 (0 self)
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This paper presents several models for investigating whether the HLA allogenotypes DR1/Br, DR3 and DR10 are genetic markers for a predisposition of experiencing unexplained recurrent fetal losses. A total of 199 women from 113 families answered questionnaires concerning their pregnancies and 145 of these women were HLA typed. The analysis of the data is complicated as dependencies between pregnancy outcomes are expected.