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61
Belief in information flow
 In Proc. 18th IEEE Computer Security Foundations Workshop
, 2005
"... Information leakage traditionally has been defined to occur when uncertainty about secret data is reduced. This uncertaintybased approach is inadequate for measuring information flow when an attacker is making assumptions about secret inputs and these assumptions might be incorrect; such attacker b ..."
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Cited by 53 (9 self)
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Information leakage traditionally has been defined to occur when uncertainty about secret data is reduced. This uncertaintybased approach is inadequate for measuring information flow when an attacker is making assumptions about secret inputs and these assumptions might be incorrect; such attacker beliefs are an unavoidable aspect of any satisfactory definition of leakage. To reason about information flow based on beliefs, a model is developed that describes how attacker beliefs change due to the attacker’s observation of the execution of a probabilistic (or deterministic) program. The model leads to a new metric for quantitative information flow that measures accuracy rather than uncertainty of beliefs. 1.
Largescale localization from wireless signal strength
 In Proc. of the National Conference on Artificial Intelligence (AAAI
, 2005
"... Knowledge of the physical locations of mobile devices such as laptops or PDA’s is becoming increasingly important with the rise of locationbased services such as specialized web search, navigation, and social network applications; furthermore, location information is a key foundation for highlevel ..."
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Cited by 46 (4 self)
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Knowledge of the physical locations of mobile devices such as laptops or PDA’s is becoming increasingly important with the rise of locationbased services such as specialized web search, navigation, and social network applications; furthermore, location information is a key foundation for highlevel activity inferencing. In this paper we propose a novel technique for accurately estimating the locations of mobile devices and their wearers from wireless signal strengths. Our technique estimates timevarying device locations on a spatial connectivity graph whose outdoor edges correspond to streets and whose indoor edges represent hallways, staircases, elevators, etc. Use of a hierarchical Bayesian framework for learning a signal strength sensor model allows us not only to achieve higher accuracy than existing approaches, but to overcome many of their limitations. In particular, our technique is able to (1) seamlessly integrate new access points into the model, (2) make use of negative information (not detecting an access point), and (3) bootstrap a sensor model from sparse training data. Experiments demonstrate various properties of our system.
Improving Robot Navigation Through SelfSupervised Online Learning
 Proceedings of Robotics: Science and Systems
, 2006
"... ..."
H: Computing Bayes factors using thermodynamic integration
 Syst Biol
"... Abstract.—In the Bayesian paradigm, a common method for comparing two models is to compute the Bayes factor, defined as the ratio of their respective marginal likelihoods. In recent phylogenetic works, the numerical evaluation of marginal likelihoods has often been performed using the harmonic mean ..."
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Cited by 34 (5 self)
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Abstract.—In the Bayesian paradigm, a common method for comparing two models is to compute the Bayes factor, defined as the ratio of their respective marginal likelihoods. In recent phylogenetic works, the numerical evaluation of marginal likelihoods has often been performed using the harmonic mean estimation procedure. In the present article, we propose to employ another method, based on an analogy with statistical physics, called thermodynamic integration. We describe the method, propose an implementation, and show on two analytical examples that this numerical method yields reliable estimates. In contrast, the harmonic mean estimator leads to a strong overestimation of the marginal likelihood, which is all the more pronounced as the model is higher dimensional. As a result, the harmonic mean estimator systematically favors more parameterrich models, an artefact that might explain some recent puzzling observations, based on harmonic mean estimates, suggesting that Bayes factors tend to overscore complex models. Finally, we apply our method to the comparison of several alternative models of aminoacid replacement. We confirm our previous observations, indicating that modeling pattern heterogeneity across sites tends to yield better models than standard empirical matrices. [Bayes factor; harmonic mean; mixture model; path sampling; phylogeny; thermodynamic integration.] Bayesian methods have become popular in molecular phylogenetics over the recent years. The simple and intuitive interpretation of the concept of probabilities
The revisiting problem in mobile robot map building: A hierarchical Bayesian approach
 In Proc. of the Conference on Uncertainty in Artificial Intelligence (UAI
, 2003
"... We present an application of hierarchical Bayesian estimation to robot map building. The revisiting problem occurs when a robot has to decide whether it is seeing a previouslybuilt portion of a map, or is exploring new territory. This is a difficult decision problem, requiring the probability of be ..."
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Cited by 26 (4 self)
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We present an application of hierarchical Bayesian estimation to robot map building. The revisiting problem occurs when a robot has to decide whether it is seeing a previouslybuilt portion of a map, or is exploring new territory. This is a difficult decision problem, requiring the probability of being outside of the current known map. To estimate this probability, we model the structure of a ”typical ” environment as a hidden Markov model that generates sequences of views observed by a robot navigating through the environment. A Dirichlet prior over structural models is learned from previously explored environments. Whenever a robot explores a new environment, the posterior over the model is estimated by Dirichlet hyperparameters. Our approach is implemented and tested in the context of multirobot map merging, a particularly difficult instance of the revisiting problem. Experiments with robot data show that the technique yields strong improvements over alternative methods. 1
Bayesian Prediction of Spatial Count Data Using Generalised Linear Mixed Models
, 2001
"... Introduction Site specic farming is aiming at targeting inputs of fertiliser, pesticide, and herbicide according to locally determined requirements. In connection with herbicide application on a eld, it is important to map the weed intensity so that the dose of herbicide applied at any location can ..."
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Cited by 25 (3 self)
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Introduction Site specic farming is aiming at targeting inputs of fertiliser, pesticide, and herbicide according to locally determined requirements. In connection with herbicide application on a eld, it is important to map the weed intensity so that the dose of herbicide applied at any location can be adjusted to the amount of weed present at the location. In a Danish project on precision farming (Olesen, 1997) one objective was to investigate whether observations of soil properties could be used for prediction of weed intensity. In practice the farmer or his advisor should then establish a relation between soil properties and weed occurrence from extensive observations collected one year and use this for prediction of the weed intensity in subsequent years where only a limited number of weed count observations would be 1 collected. Many soil properties are fairly constant over time so that observations of soil samples obtained the rst year can also be used in subseq
Bayesian color estimation for adaptive visionbased robot localization
 in IROS
, 2004
"... Abstract — In this article we introduce a hierarchical Bayesian model to estimate a set of colors with a mobile robot. Estimating colors is particularly important if objects in an environment can only be distinguished by their color. Since the appearance of colors can change due to variations in the ..."
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Cited by 24 (0 self)
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Abstract — In this article we introduce a hierarchical Bayesian model to estimate a set of colors with a mobile robot. Estimating colors is particularly important if objects in an environment can only be distinguished by their color. Since the appearance of colors can change due to variations in the lighting condition, a robot needs to adapt its color model to such changes. We propose a two level Gaussian model in which the lighting conditions are estimated at the upper level using a switching Kalman filter. A hierarchical Bayesian technique learns Gaussian priors from data collected in other environments. Furthermore, since estimation of the color model depends on knowledge of the robot’s location, we employ a RaoBlackwellised particle filter to maintain a joint posterior over robot positions and lighting conditions. We evaluate the technique in the context of the RoboCup AIBO league, where a legged AIBO robot has to localize itself in an environment similar to a soccer field. Our experiments show that the robot can localize under different lighting conditions and adapt to changes in the lighting condition, for example, due to a light being turned on or off. I.
Foundations of Assisted Cognition Systems
, 2003
"... this report. Kautz [79] modeled plan recognition logically in a manner that allowed goals and plans to be described at various levels of abstraction. Etzioni et al. [94, 95, 92, 93] developed a version space algorithm for plan recognition that is provably sound and polynomial time [94, 93]. Weld et ..."
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Cited by 22 (3 self)
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this report. Kautz [79] modeled plan recognition logically in a manner that allowed goals and plans to be described at various levels of abstraction. Etzioni et al. [94, 95, 92, 93] developed a version space algorithm for plan recognition that is provably sound and polynomial time [94, 93]. Weld et al. developed goal recognition algorithms using inductive logic programming [90] and versionspace algebra [89, 168, 88] in the context of programming by demonstration
Robust MultiTask Learning with tProcesses
"... Most current multitask learning frameworks ignore the robustness issue, which means that the presence of “outlier ” tasks may greatly reduce overall system performance. We introduce a robust framework for Bayesian multitask learning, tprocesses (TP), which are a generalization of Gaussian processe ..."
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Cited by 19 (0 self)
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Most current multitask learning frameworks ignore the robustness issue, which means that the presence of “outlier ” tasks may greatly reduce overall system performance. We introduce a robust framework for Bayesian multitask learning, tprocesses (TP), which are a generalization of Gaussian processes (GP) for multitask learning. TP allows the system to effectively distinguish good tasks from noisy or outlier tasks. Experiments show that TP not only improves overall system performance, but can also serve as an indicator for the “informativeness ” of different tasks. 1.
Markov Chain Monte Carlo for Statistical Inference
 University of Washington, Center for
, 2000
"... These notes provide an introduction to Markov chain Monte Carlo methods that are useful in both Bayesian and frequent... ..."
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Cited by 18 (0 self)
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These notes provide an introduction to Markov chain Monte Carlo methods that are useful in both Bayesian and frequent...