Results 1  10
of
128
Operations for Learning with Graphical Models
 Journal of Artificial Intelligence Research
, 1994
"... This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Wellknown examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models ..."
Abstract

Cited by 246 (12 self)
 Add to MetaCart
This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Wellknown examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feedforward networks, and learning Gaussian and discrete Bayesian networks from data. The paper conclu...
Bayesian Networks Without Tears
 AI MAGAZINE
, 1991
"... I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community. Indeed, it is probably fair to say that Bayesia ..."
Abstract

Cited by 235 (2 self)
 Add to MetaCart
I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community. Indeed, it is probably fair to say that Bayesian networks are to a large segment of the AIuncertainty community what resolution theorem proving is to the AIlogic community. Nevertheless, despite what seems to be their obvious importance, the ideas and techniques have not spread much beyond the research community responsible for them. This is probably because the ideas and techniques are not that easy to understand. I hope to rectify this situation by making Bayesian networks more accessible to the probabilistically unsophisticated.
Adaptive Probabilistic Networks with Hidden Variables
 Machine Learning
, 1997
"... . Probabilistic networks (also known as Bayesian belief networks) allow a compact description of complex stochastic relationships among several random variables. They are rapidly becoming the tool of choice for uncertain reasoning in artificial intelligence. In this paper, we investigate the problem ..."
Abstract

Cited by 158 (10 self)
 Add to MetaCart
. Probabilistic networks (also known as Bayesian belief networks) allow a compact description of complex stochastic relationships among several random variables. They are rapidly becoming the tool of choice for uncertain reasoning in artificial intelligence. In this paper, we investigate the problem of learning probabilistic networks with known structure and hidden variables. This is an important problem, because structure is much easier to elicit from experts than numbers, and the world is rarely fully observable. We present a gradientbased algorithmand show that the gradient can be computed locally, using information that is available as a byproduct of standard probabilistic network inference algorithms. Our experimental results demonstrate that using prior knowledge about the structure, even with hidden variables, can significantly improve the learning rate of probabilistic networks. We extend the method to networks in which the conditional probability tables are described using a ...
From Influence Diagrams to Junction Trees
 PROCEEDINGS OF THE TENTH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
, 1994
"... We present an approach to the solution of decision problems formulated as influence diagrams. This approach involves a special triangulation of the underlying graph, the construction of a junction tree with special properties, and a message passing algorithm operating on the junction tree for comput ..."
Abstract

Cited by 108 (15 self)
 Add to MetaCart
We present an approach to the solution of decision problems formulated as influence diagrams. This approach involves a special triangulation of the underlying graph, the construction of a junction tree with special properties, and a message passing algorithm operating on the junction tree for computation of expected utilities and optimal decision policies.
Towards Robust Automatic Traffic Scene Analysis in RealTime
, 1994
"... Automatic symbolic traffic scene analysis is essential to many areas of IVHS (Intelligent Vehicle Highway Systems). Traffic scene information can be used to optimize traffic flow during busy periods, identify stalled vehicles and accidents, and aid the decisionmaking of an autonomous vehicle control ..."
Abstract

Cited by 92 (4 self)
 Add to MetaCart
Automatic symbolic traffic scene analysis is essential to many areas of IVHS (Intelligent Vehicle Highway Systems). Traffic scene information can be used to optimize traffic flow during busy periods, identify stalled vehicles and accidents, and aid the decisionmaking of an autonomous vehicle controller. Improvements in technologies for machine visionbased surveillance and highlevel symbolic reasoning have enabled us to develop a system for detailed, reliable traffic scene analysis. The machine vision component of our system employs a contour tracker and an affine motion model based on Kalman filters to extract vehicle trajectories over a sequence of traffic scene images. The symbolic reasoning component uses a dynamic belief network to make inferences about traffic events such as vehicle lane changes and stalls. In this paper, we discuss the key tasks of the vision and reasoning components as well as their integration into a working prototype. Preliminary results of an implementation on special purpose hardware using C40 Digital Signal Processors show that near realtime performance can be achieved without further improvements.
Perspectives on the Theory and Practice of Belief Functions
 International Journal of Approximate Reasoning
, 1990
"... The theory of belief functions provides one way to use mathematical probability in subjective judgment. It is a generalization of the Bayesian theory of subjective probability. When we use the Bayesian theory to quantify judgments about a question, we must assign probabilities to the possible answer ..."
Abstract

Cited by 85 (7 self)
 Add to MetaCart
The theory of belief functions provides one way to use mathematical probability in subjective judgment. It is a generalization of the Bayesian theory of subjective probability. When we use the Bayesian theory to quantify judgments about a question, we must assign probabilities to the possible answers to that question. The theory of belief functions is more flexible; it allows us to derive degrees of belief for a question from probabilities for a related question. These degrees of belief may or may not have the mathematical properties of probabilities; how much they differ from probabilities will depend on how closely the two questions are related. Examples of what we would now call belieffunction reasoning can be found in the late seventeenth and early eighteenth centuries, well before Bayesian ideas were developed. In 1689, George Hooper gave rules for combining testimony that can be recognized as special cases of Dempster's rule for combining belief functions (Shafer 1986a). Similar rules were formulated by Jakob Bernoulli in his Ars Conjectandi, published posthumously in 1713, and by JohannHeinrich Lambert in his Neues Organon, published in 1764 (Shafer 1978). Examples of belieffunction reasoning can also be found in more recent work, by authors
Construction of Bayesian Network Structures From Data: A Brief Survey and an Efficient Algorithm
, 1995
"... Previous algorithms for the recovery of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required on ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches: CI tests ..."
Abstract

Cited by 77 (8 self)
 Add to MetaCart
Previous algorithms for the recovery of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required on ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches: CI tests are used to generate an ordering on the nodes from the database, which is then used to recover the underlying Bayesian network structure using a nonCltestbased method. Results of the evaluation of the algorithm on a number of databases (e.g., ALARM, LED, and SOYBEAN) are presented. We also discuss some algorithm performance issues and open problems.
Learning Bayesian Networks by Genetic Algorithms. A case study in the prediction of survival in malignant skin melanoma
, 1997
"... In this work we introduce a methodology based on Genetic Algorithms for the automatic induction of Bayesian Networks from a file containing cases and variables related to the problem. The methodology is applied to the problem of predicting survival of people after one, three and five years of being ..."
Abstract

Cited by 71 (11 self)
 Add to MetaCart
In this work we introduce a methodology based on Genetic Algorithms for the automatic induction of Bayesian Networks from a file containing cases and variables related to the problem. The methodology is applied to the problem of predicting survival of people after one, three and five years of being diagnosed as having malignant skin melanoma. The accuracy of the obtained model, measured in terms of the percentage of wellclassified subjects, is compared to that obtained by the called NaiveBayes. In both cases, the estimation of the model accuracy is obtained from the 10fold crossvalidation method. 1. Introduction Expert systems, one of the most developed areas in the field of Artificial Intelligence, are computer programs designed to help or replace humans beings in tasks in which the human experience and human knowledge are scarce and unreliable. Although, there are domains in which the tasks can be specifed by logic rules, other domains are characterized by an uncertainty inherent...
Automatic symbolic traffic scene analysis using belief networks
 PROCEEDINGS 12TH NATIONAL CONFERENCE IN AI
, 1994
"... Automatic symbolic traffic scene analysis is essential to many areas of IVHS (Intelligent Vehicle Highway Systems). Traffic scene information can be used to optimize traffic flow during busy periods, identify stalled vehicles and accidents, and aid the decisionmaking of an autonomous vehicle contro ..."
Abstract

Cited by 67 (8 self)
 Add to MetaCart
Automatic symbolic traffic scene analysis is essential to many areas of IVHS (Intelligent Vehicle Highway Systems). Traffic scene information can be used to optimize traffic flow during busy periods, identify stalled vehicles and accidents, and aid the decisionmaking of an autonomous vehicle controller. Improvements in technologies for machine visionbased surveillance and highlevel symbolic reasoning have enabled us to develop a system for detailed, reliable traffic scene analysis. The machine vision component of our system employs a contour tracker and an a fine motion model based on Kalman filters to extract vehicle trajectories over a sequence of traffic scene images. The symbolic reasoning component uses a dynamic belief network to make inferences about traffic events such as vehicle lane changes and stalls. In this paper, we discuss the key tasks of the vision and reasoning components as well as their integration into a working prototype.
An efficient algorithm for finding the M most probable configurations in probabilistic expert systems
 Statistics and Computing
, 1998
"... A probabilistic expert system provides a graphical representation of a joint probability distribution which enables local computations of probabilities. Dawid (1992) provided a `flowpropagation' algorithm for finding the most probable configuration of the joint distribution in such a system. This p ..."
Abstract

Cited by 66 (3 self)
 Add to MetaCart
A probabilistic expert system provides a graphical representation of a joint probability distribution which enables local computations of probabilities. Dawid (1992) provided a `flowpropagation' algorithm for finding the most probable configuration of the joint distribution in such a system. This paper analyses that algorithm in detail, and shows how it can be combined with a clever partitioning scheme to formulate an efficient method for finding the M most probable configurations. The algorithm is a divide and conquer technique, that iteratively identifies the M most probable configurations. The algorithm has been implemented into the experimental shell XBAIES, which is an extension of BAIES (Cowell, 1992). Keywords: Bayesian network, belief revision, most probable explanation, junction tree, maximization, propagation, charge, potential function, conditional independence, flow, evidence, marginalization, divideandconquer. 1 Introduction A probabilistic expert system (PES) funct...