An Algorithm to Define the Node Probability Functions of Bayesian Networks based on Ranked Nodes

Nunes, Joa˜o Raza and Gustavo; Willamy, Renan; Perkusich, Mirko; Saraiva, Renata M.; Gorgônio, Kyller Costa; Almeida, Hyggo; Perkusich, Angelo

doi:10.14445/22315381/ijett-v52p223

Cited by 1 publication

(2 citation statements)

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“…In [22], it is proposed a probabilistic algorithm for this purpose, composed of two main steps: (i) generate samples for the parent nodes and Figure 6. Examples of TNormal.…”

Section: Rnmmentioning

confidence: 99%

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

Nunes¹,

Barbosa²,

Silva³

et al. 2018

Enhanced Expert Systems [Working Title]

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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.

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“…In [22], it is proposed a probabilistic algorithm for this purpose, composed of two main steps: (i) generate samples for the parent nodes and Figure 6. Examples of TNormal.…”

Section: Rnmmentioning

confidence: 99%

“…For instance, for 100% "Good", it is collected samples of a uniform distribution limited in the interval [2/3, 1]. In [22] it is empirically defined that using a sample size of 10,000 is enough to guarantee a margin of error less than 0.1%. Each sample is registered with meta-data regarding its configuration (i.e., number of states and μ).…”

Section: Rnmmentioning

confidence: 99%