Results 1  10
of
70
TRAVOS: Trust and reputation in the context of inaccurate information sources
 Journal of Autonomous Agents and MultiAgent Systems
, 2006
"... Abstract. In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be selfinterested and when trusted to perform an action for another, may betray that trust by not performing the action as required. In addition, due to the size of such systems ..."
Abstract

Cited by 87 (15 self)
 Add to MetaCart
Abstract. In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be selfinterested and when trusted to perform an action for another, may betray that trust by not performing the action as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. There is therefore a need to develop a model of trust and reputation that will ensure good interactions among software agents in large scale open systems. Against this background, we have developed TRAVOS (Trust and Reputation model for Agentbased Virtual OrganisationS) which models an agent’s trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents, and when there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate. 1
Bayesian hierarchical clustering
 In Proceedings of the 22nd International Conference on Machine Learning. ACM
, 2005
"... We present a novel algorithm for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. This algorithm has several advantages over traditional distancebased agglomerative clustering algorithms. (1) It defines a probabilistic model of the data which ..."
Abstract

Cited by 46 (9 self)
 Add to MetaCart
We present a novel algorithm for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. This algorithm has several advantages over traditional distancebased agglomerative clustering algorithms. (1) It defines a probabilistic model of the data which can be used to compute the predictive distribution of a test point and the probability of it belonging to any of the existing clusters in the tree. (2) It uses a modelbased criterion to decide on merging clusters rather than an adhoc distance metric. (3) Bayesian hypothesis testing is used to decide which merges are advantageous and to output the recommended depth of the tree. (4) The algorithm can be interpreted as a novel fast bottomup approximate inference method for a Dirichlet process (i.e. countably infinite) mixture model (DPM). It provides a new lower bound on the marginal likelihood of a DPM by summing over exponentially many clusterings of the data in polynomial time. We describe procedures for learning the model hyperparameters, computing the predictive distribution, and extensions to the algorithm. Experimental results on synthetic and realworld data sets demonstrate useful properties of the algorithm. 1.
A quantitative study of gene regulation involved in the immune response of Anopheline mosquitoes: An application of Bayesian hierarchical clustering of curves
 Journal of the American Statistical Association
, 2006
"... Malaria represents one of the major worldwide challenges to public health. A recent breakthrough in the study of the disease follows the annotation of the genome of the malaria parasite Plasmodium falciparum and the mosquito vector 1 Anopheles. Of particular interest is the molecular biology underly ..."
Abstract

Cited by 39 (2 self)
 Add to MetaCart
Malaria represents one of the major worldwide challenges to public health. A recent breakthrough in the study of the disease follows the annotation of the genome of the malaria parasite Plasmodium falciparum and the mosquito vector 1 Anopheles. Of particular interest is the molecular biology underlying the immune response system of Anopheles which actively fights against Plasmodium infection. This paper reports a statistical analysis of gene expression time profiles from mosquitoes which have been infected with a bacterial agent. Specifically, we introduce a Bayesian modelbased hierarchical clustering algorithm for curve data to investigate mechanisms of regulation in the genes concerned; that is, we aim to cluster genes having similar expression profiles. Genes displaying similar, interesting profiles can then be highlighted for further investigation by the experimenter. We show how our approach reveals structure within the data not captured by other approaches. One of the most pertinent features of the data is the sample size, which records the expression levels of 2771 genes at six time points. Additionally, the time points are unequally spaced and there is expected nonstationary behaviour in the gene profiles. We demonstrate our approach to be readily implementable under these conditions, and highlight some crucial computational savings that can be made in the context of a fully Bayesian analysis.
Modeling changing dependency structure in multivariate time series
 In International Conference in Machine Learning
, 2007
"... We show how to apply the efficient Bayesian changepoint detection techniques of Fearnhead in the multivariate setting. We model the joint density of vectorvalued observations using undirected Gaussian graphical models, whose structure we estimate. We show how we can exactly compute the MAP segmenta ..."
Abstract

Cited by 31 (0 self)
 Add to MetaCart
We show how to apply the efficient Bayesian changepoint detection techniques of Fearnhead in the multivariate setting. We model the joint density of vectorvalued observations using undirected Gaussian graphical models, whose structure we estimate. We show how we can exactly compute the MAP segmentation, as well as how to draw perfect samples from the posterior over segmentations, simultaneously accounting for uncertainty about the number and location of changepoints, as well as uncertainty about the covariance structure. We illustrate the technique by applying it to financial data and to bee tracking data. 1.
Semisupervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery,” IRIT/ENSEEIHT/TeSA
, 2007
"... Abstract—This paper proposes a hierarchical Bayesian model that can be used for semisupervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive Gaussian noise. The abundance parameters a ..."
Abstract

Cited by 31 (21 self)
 Add to MetaCart
Abstract—This paper proposes a hierarchical Bayesian model that can be used for semisupervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive Gaussian noise. The abundance parameters appearing in this model satisfy positivity and additivity constraints. These constraints are naturally expressed in a Bayesian context by using appropriate abundance prior distributions. The posterior distributions of the unknown model parameters are then derived. A Gibbs sampler allows one to draw samples distributed according to the posteriors of interest and to estimate the unknown abundances. An extension of the algorithm is finally studied for mixtures with unknown numbers of spectral components belonging to a know library. The performance of the different unmixing strategies is evaluated via simulations conducted on synthetic and real data. Index Terms—Gibbs sampler, hierarchical Bayesian analysis, hyperspectral images, linear spectral unmixing, Markov chain Monte Carlo (MCMC) methods, reversible jumps. I.
Spatial modelling using a new class of nonstationary covariance functions
 Environmetrics
, 2006
"... We introduce a new class of nonstationary covariance functions for spatial modelling. Nonstationary covariance functions allow the model to adapt to spatial surfaces whose variability changes with location. The class includes a nonstationary version of the Matérn stationary covariance, in which the ..."
Abstract

Cited by 27 (0 self)
 Add to MetaCart
We introduce a new class of nonstationary covariance functions for spatial modelling. Nonstationary covariance functions allow the model to adapt to spatial surfaces whose variability changes with location. The class includes a nonstationary version of the Matérn stationary covariance, in which the differentiability of the spatial surface is controlled by a parameter, freeing one from fixing the differentiability in advance. The class allows one to knit together local covariance parameters into a valid global nonstationary covariance, regardless of how the local covariance structure is estimated. We employ this new nonstationary covariance in a fully Bayesian model in which the unknown spatial process has a Gaussian process (GP) distribution with a nonstationary covariance function from the class. We model the nonstationary structure in a computationally efficient way that creates nearly stationary local behavior and for which stationarity is a special case. We also suggest nonBayesian approaches to nonstationary kriging. To assess the method, we compare the Bayesian nonstationary GP model with a Bayesian stationary GP model, various standard spatial smoothing approaches, and nonstationary models that can adapt to function heterogeneity. In simulations, the nonstationary GP model adapts to function heterogeneity, unlike the stationary models, and also outperforms the other nonstationary models. On a real dataset, GP models outperform the competitors, but while the nonstationary GP gives qualitatively more sensible results, it fails to outperform the stationary GP on heldout data, illustrating the difficulty in fitting complex spatial functions with relatively few observations. The nonstationary covariance model could also be used for nonGaussian data and embedded in additive models as well as in more complicated, hierarchical spatial or spatiotemporal models. More complicated models may require simpler parameterizations for computational efficiency.
Transdimensional Markov Chains: A Decade of Progress and Future Perspectives
 Journal of the American Statistical Association
, 2005
"... The last ten years have witnessed the development of sampling frameworks that permit the construction of Markov chains which simultaneously traverse both parameter and model space. In this time substantial methodological progress has been made. In this article we present a survey of the current stat ..."
Abstract

Cited by 18 (2 self)
 Add to MetaCart
The last ten years have witnessed the development of sampling frameworks that permit the construction of Markov chains which simultaneously traverse both parameter and model space. In this time substantial methodological progress has been made. In this article we present a survey of the current state of the art and evaluate some of the most recent advances in this field. We also discuss future research perspectives in the context of the drive to develop sampling mechanisms with high degrees of both efficiency and automation. 1
Bayesian mixed membership models for soft clustering and classification
 Classification—The Ubiquitous Challenge
, 2005
"... work was presented in a plenary lecture at the 28th Annual Conference of the German Classification Society, Dortmund. We are indebted to John Lafferty for his collaboration on the analysis of the PNAS data which we report here, and to Adrian Raftery and Christian Robert for helpful discussions on se ..."
Abstract

Cited by 18 (6 self)
 Add to MetaCart
work was presented in a plenary lecture at the 28th Annual Conference of the German Classification Society, Dortmund. We are indebted to John Lafferty for his collaboration on the analysis of the PNAS data which we report here, and to Adrian Raftery and Christian Robert for helpful discussions on selecting K. Erosheva’s work was supported by NIH grants 1 RO1 AG02314101 and R01 CA9421201, Fienberg’s work was supported by NIH grant 1 RO1 AG02314101 and by the Centre de Recherche en Economie et Statistique of the Institut National de la Statistique et
Exploratory Data Analysis for Complex Models
, 2002
"... Exploratory" and "confirmatory" data analysis can both be viewed as methods for comparing observed data to what would be obtained under an implicit or explicit statistical model. ..."
Abstract

Cited by 14 (6 self)
 Add to MetaCart
Exploratory" and "confirmatory" data analysis can both be viewed as methods for comparing observed data to what would be obtained under an implicit or explicit statistical model.