Approximate probability propagation with mixtures of truncated exponentials

AbstractMixtures of truncated exponentials (MTEs) are a powerful alternative to discretisation when working with hybrid Bayesian networks. One of the features of the MTE model is that standard propagation algorithms can be used. However, the complexity of the process is too high and therefore approximate methods, which tradeoff complexity for accuracy, become necessary. In this paper we propose an approximate propagation algorithm for MTE networks which is based on the Penniless propagation method already known for discrete variables. We also consider how to use Markov Chain Monte Carlo to carry out the probability propagation. The performance of the proposed methods is analysed in a series of experiments with random networks

Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions

Causal Probabilistic Networks With Both Discrete and Continuous Variables

An extension of the expert system shell HUGIN to include continuous wriables, in the form of linear additive normally distributed variables, is presented. Th

Stochastische Behandlung von Unsicherheiten in kaskadierten dynamischen Systemen

In dieser Arbeit wird die Idee verfolgt, komplexe Systeme aus sehr einfachen Teilsystemen aufzubauen und für solche Systemkaskaden eine stochastische Zustandsschätzung durchzuführen. Dabei wird die Struktur der Kaskade verwendet, um die Schätzung lokal in den Teilsystemen durchzuführen woraus eine globale Schätzung abgeleitet wird. Im Fokus der Arbeit stehen nichtlineare und hybride Systeme. Als eine Anwendung wird die Intentionserkennung in der Mensch-Roboter-Kooperation betrachtet

Causal Probabilistic Networks With Both Discrete and Continuous Variables

An extension of the expert system shell HUGIN to include continuous variables, in the form of linear additive normally distributed variables, is presented. The theoretical foundation of the method was developed by Lauritzen (1992), whereas this report primarily focus on implementation aspects. The approach has several advantages over purely discrete systems: It enables a more natural model of the domain in question, knowledge acquisition is eased and the complexity of belief revision is most often reduced considerably. 1 Introduction In recent years much attention has been directed towards probabilistic reasoning in graphical models. Graphical models with directed edges are known under various synonyms, for example belief networks (Pearl 1988) or causal probabilistic networks (Andreassen et al. 1987). In this paper the term causal probabilistic network, or CPN for short, will be used. Several algorithms for inference in CPNs have been proposed (Pearl 1986, 1988; Shachter 1986; Lauritz..

Local Probability Distributions in Bayesian Networks: Knowledge Elicitation and Inference

Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowledge and have been applied successfully in many domains for over 25 years. The strength of Bayesian networks lies in the graceful combination of probability theory and a graphical structure representing probabilistic dependencies among domain variables in a compact manner that is intuitive for humans. One major challenge related to building practical BN models is specification of conditional probability distributions. The number of probability distributions in a conditional probability table for a given variable is exponential in its number of parent nodes, so that defining them becomes problematic or even impossible from a practical standpoint. The objective of this dissertation is to develop a better understanding of models for compact representations of local probability distributions. The hypothesis is that such models should allow for building larger models more efficiently and lead to a wider range of BN applications

Visual recognition of multi-agent action

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1999.Includes bibliographical references (p. 167-184).Developing computer vision sensing systems that work robustly in everyday environments will require that the systems can recognize structured interaction between people and objects in the world. This document presents a new theory for the representation and recognition of coordinated multi-agent action from noisy perceptual data. The thesis of this work is as follows: highly structured, multi-agent action can be recognized from noisy perceptual data using visually grounded goal-based primitives and low-order temporal relationships that are integrated in a probabilistic framework. The theory is developed and evaluated by examining general characteristics of multi-agent action, analyzing tradeoffs involved when selecting a representation for multi-agent action recognition, and constructing a system to recognize multi-agent action for a real task from noisy data. The representation, which is motivated by work in model-based object recognition and probabilistic plan recognition, makes four principal assumptions: (1) the goals of individual agents are natural atomic representational units for specifying the temporal relationships between agents engaged in group activities, (2) a high-level description of temporal structure of the action using a small set of low-order temporal and logical constraints is adequate for representing the relationships between the agent goals for highly structured, multi-agent action recognition, (3) Bayesian networks provide a suitable mechanism for integrating multiple sources of uncertain visual perceptual feature evidence, and (4) an automatically generated Bayesian network can be used to combine uncertain temporal information and compute the likelihood that a set of object trajectory data is a particular multi-agent action. The recognition algorithm is tested using a database of American football play descriptions. A system is described that can recognize single-agent and multi-agent actions in this domain given noisy trajectories of object movements. The strengths and limitations of the recognition system are discussed and compared with other multi-agent recognition algorithms.by Stephen Sean Intille.Ph.D