Construction of Bayesian Models (original) (raw)

Challenge: Where is the Impact of Bayesian Networks in

Proceedings of the …, 1997

Bayesian networks are graphical representations of probability distributions. Over the last decade, these representations have become the method of choice for representation of uncertainly in arti cial intelligence. Today, they play a crucial role in modern expert systems, diagnosis engines, and decision support systems. In recent years, there has been much interest in learning Bayesian networks from data. Learning such models is desirable simply because there is a wide array of o -the-shelf tools that can apply the learned models as described above. Practitioners also claim that adaptive Bayesian networks have advantages in their own right as a non-parametric method for density estimation, data analysis, pattern classication, and modeling. Among the reasons cited we nd: their semantic clarity and understandability by humans, the ease of acquisition and incorporation of prior knowledge, the ease of integration with optimal decision-making methods, the possibility of causal interpretation of learned models, and the automatic handling of noisy and missing data. In spite of these claims, methods that learn Bayesian networks have yet to make the impact that other techniques such as neural networks and hidden Markov models have made in applications such as pattern and speech recognition. In this paper, we challenge the research community to identify and characterize domains where induction of Bayesian networks makes the critical di erence, and to quantify the factors that are responsible for that di erence. In addition to formalizing the challenge, we identify research problems whose solution is, in our view, crucial for meeting this challenge.

Issues in the Probability Elicitation Process of Expert-Based Bayesian Networks

Enhanced Expert Systems [Working Title], 2018

A major challenge in constructing a Bayesian network (BN) is defining the node probability tables (NPT), which can be learned from data or elicited from domain experts. In practice, it is common not to have enough data for learning, and elicitation from experts is the only option. However, the complexity of defining NPT grows exponentially, making their elicitation process costly and error-prone. In this research, we conducted an exploratory study through a literature review that identified the main issues related to the task of probability elicitation and solutions to construct large-scale NPT while reducing the exposure to these issues. In this chapter, we present in detail three semiautomatic methods that reduce the burden for experts. We discuss the benefits and drawbacks of these methods, and present directions on how to improve them.

Introducing Bayesian Networks 2.1 Introduction

Having presented both theoretical and practical reasons for artificial intelligence to use probabilistic reasoning, we now introduce the key computer technology for dealing with probabilities in AI, namely Bayesian networks. Bayesian networks (BNs) are graphical models for reasoning under uncertainty, where the nodes represent variables (discrete or continuous) and arcs represent direct connections between them. These direct connections are often causal connections. In addition, BNs model the quantitative strength of the connections between variables, allowing probabilistic beliefs about them to be updated automatically as new information becomes available. In this chapter we will describe how Bayesian networks are put together (the syntax) and how to interpret the information encoded in a network (the semantics). We will look at how to model a problem with a Bayesian network and the types of reasoning that can be performed.

On Selecting the Optimal Bayesian Network Model Construction Approach

2012

The construction of a Bayesian Network (BN) model entails two major tasks: realization of the model structure, and the calibration (parameterization) of the model. BN model constructors, ab initio, relied only on domain experts to define both the structure and parameters of a model. Currently, algorithms exist to construct BN models from data. Consequently, there are three BN model construction techniques: total expert-centred, total data-centred, and semi data-centred. We empirically investigated which of these approaches is the optimal approach for the construction of a BN model for our intended application. The investigation yielded some interesting themes.

Relieving the elicitation burden of Bayesian Belief Networks

Conference: Proceedings of the Sixth UAI Bayesian Modelling Applications WorkshopAt: Helsinki, Finland, July 9, 2008

In this paper we present a new method (EBBN) that aims at reducing the need to elicit formidable amounts of probabilities for Bayesian belief networks, by reducing the number of probabilities that need to be speci- fied in the quantification phase. This method enables the derivation of a variable's condi- tional probability table (CPT) in the gen- eral case that the states of the variable are ordered and the states of each of its parent nodes can be ordered with respect to the in- fluence they exercise. EBBN requires only a limited amount of probability assessments from experts to determine a variable's full CPT and uses piecewise linear interpolation. The number of probabilities to be assessed in this method is linear in the number of condi- tioning variables. EBBN's performance was compared with the results achieved by ap- plying both the normal copula vine approach from Hanea & Kurowicka (2007), and by us- ing a simple uniform distribution.