Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks

The discrete-time Distributed Bayesian Filtering (DBF) algorithm is presented for the problem of tracking a target dynamic model using a time-varying network of heterogeneous sensing agents. In the DBF algorithm, the sensing agents combine their normalized likelihood functions in a distributed manner using the logarithmic opinion pool and the dynamic average consensus algorithm. We show that each agent's estimated likelihood function globally exponentially converges to an error ball centered on the joint likelihood function of the centralized multi-sensor Bayesian filtering algorithm. We rigorously characterize the convergence, stability, and robustness properties of the DBF algorithm. Moreover, we provide an explicit bound on the time step size of the DBF algorithm that depends on the time-scale of the target dynamics, the desired convergence error bound, and the modeling and communication error bounds. Furthermore, the DBF algorithm for linear-Gaussian models is cast into a modified form of the Kalman information filter. The performance and robust properties of the DBF algorithm are validated using numerical simulations

Recognition of Face Identity and Emotion in Expressive Specific Language Impairment

Objective: To study face and emotion recognition in children with mostly expressive specific language impairment (SLI-E). Subjects and Methods: A test movie to study perception and recognition of faces and mimic-gestural expression was applied to 24 children diagnosed as suffering from SLI-E and an age-matched control group of normally developing children. Results: Compared to a normal control group, the SLI-E children scored significantly worse in both the face and expression recognition tasks with a preponderant effect on emotion recognition. The performance of the SLI-E group could not be explained by reduced attention during the test session. Conclusion: We conclude that SLI-E is associated with a deficiency in decoding non-verbal emotional facial and gestural information, which might lead to profound and persistent problems in social interaction and development. Copyright (C) 2012 S. Karger AG, Base

Blackboard Rules for Coordinating Context-aware Applications in Mobile Ad Hoc Networks

Thanks to improvements in wireless communication technologies and increasing computing power in hand-held devices, mobile ad hoc networks are becoming an ever-more present reality. Coordination languages are expected to become important means in supporting this type of interaction. To this extent we argue the interest of the Bach coordination language as a middleware that can handle and react to context changes as well as cope with unpredictable physical interruptions that occur in opportunistic network connections. More concretely, our proposal is based on blackboard rules that model declaratively the actions to be taken once the blackboard content reaches a predefined state, but also that manage the engagement and disengagement of hosts and transient sharing of blackboards. The idea of reactiveness has already been introduced in previous work, but as will be appreciated by the reader, this article presents a new perspective, more focused on a declarative setting.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432

Conditionally externally Bayesian pooling operators in chain graphs

We address the multivariate version of French’s group decision problem where the m members of a group, who are jointly responsible for the decisions they should make, wish to combine their beliefs about the possible values of n random variables into the group consensus probability distribution. We shall assume the group has agreed on the structure of associations of variables in a problem, as might be represented by a commonly agreed partially complete chain graph (PCG) we define in the paper. However, the members diverge about the actual conditional probability distributions for the variables in the common PCG. The combination algorithm we suggest they adopt is one which demands, at least on learning information which is common to the members and which preserves the originally agreed PCG structure, that the pools of conditional distributions associated with the PCG are externally Bayesian (EB). We propose a characterization for such conditionally EB (CEB) poolings which is more general and flexible than the characterization proposed by Genest, McConway and Schervish. In particular, such a generalization allows the weights attributed to the joint probability assessments of different individuals in the pool to differ across the distinct components of each joint density. We show that the group’s commitment to being CEB on chain elements can be accomplished by the group being EB on the whole PCG when the group also agrees to perform the conditional poolings in an ordering compatible with evidence propagation in the graph

Reliability of perceptions of voice quality: evidence from a problem asthma clinic population

<p>Introduction: Methods of perceptual voice evaluation have yet to achieve satisfactory consistency; complete acceptance of a recognised clinical protocol is still some way off.</p> <p>Materials and methods: Three speech and language therapists rated the voices of 43 patients attending the problem asthma clinic of a teaching hospital, according to the grade-roughness-breathiness-asthenicity-strain (GRBAS) scale and other perceptual categories.</p> <p>Results and analysis: Use of the GRBAS scale achieved only a 64.7 per cent inter-rater reliability and a 69.6 per cent intra-rater reliability for the grade component. One rater achieved a higher degree of consistency. Improved concordance on the GRBAS scale was observed for subjects with laryngeal abnormalities. Raters failed to reach any useful level of agreement in the other categories employed, except for perceived gender.</p> <p>Discussion: These results should sound a note of caution regarding routine adoption of the GRBAS scale for characterising voice quality for clinical purposes. The importance of training and the use of perceptual anchors for reliable perceptual rating need to be further investigated.</p&gt

Reliable Uncertain Evidence Modeling in Bayesian Networks by Credal Networks

A reliable modeling of uncertain evidence in Bayesian networks based on a set-valued quantification is proposed. Both soft and virtual evidences are considered. We show that evidence propagation in this setup can be reduced to standard updating in an augmented credal network, equivalent to a set of consistent Bayesian networks. A characterization of the computational complexity for this task is derived together with an efficient exact procedure for a subclass of instances. In the case of multiple uncertain evidences over the same variable, the proposed procedure can provide a set-valued version of the geometric approach to opinion pooling.Comment: 19 page

Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to nuclear emergency management

Although many decision-making problems involve uncertainty, uncertainty handling within large decision support systems (DSSs) is challenging. One domain where uncertainty handling is critical is emergency response management, in particular nuclear emergency response, where decision making takes place in an uncertain, dynamically changing environment. Assimilation and analysis of data can help to reduce these uncertainties, but it is critical to do this in an efficient and defensible way. After briefly introducing the structure of a typical DSS for nuclear emergencies, the paper sets up a theoretical structure that enables a formal Bayesian decision analysis to be performed for environments like this within a DSS architecture. In such probabilistic DSSs many input conditional probability distributions are provided by different sets of experts overseeing different aspects of the emergency. These probabilities are then used by the decision maker (DM) to find her optimal decision. We demonstrate in this paper that unless due care is taken in such a composite framework, coherence and rationality may be compromised in a sense made explicit below. The technology we describe here builds a framework around which Bayesian data updating can be performed in a modular way, ensuring both coherence and efficiency, and provides sufficient unambiguous information to enable the DM to discover her expected utility maximizing policy

Distributed Estimation using Bayesian Consensus Filtering

We present the Bayesian consensus filter (BCF) for tracking a moving target using a networked group of sensing agents and achieving consensus on the best estimate of the probability distributions of the target’s states. Our BCF framework can incorporate nonlinear target dynamic models, heterogeneous nonlinear measurement models, non-Gaussian uncertainties, and higher-order moments of the locally estimated posterior probability distribution of the target’s states obtained using Bayesian filters. If the agents combine their estimated posterior probability distributions using a logarithmic opinion pool, then the sum of Kullback–Leibler divergences between the consensual probability distribution and the local posterior probability distributions is minimized. Rigorous stability and convergence results for the proposed BCF algorithm with single or multiple consensus loops are presented. Communication of probability distributions and computational methods for implementing the BCF algorithm are discussed along with a numerical example