Citation

Karimnezhad A, Moradi F. Transp. Lett. 2017; 9(1): 12-19.

Copyright

(Copyright © 2017, Maney Publishing, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19427867.2015.1131960

PMID

unavailable

Abstract

Bayesian Networks (BNs) are graphical probabilistic models representing the joint probability function over a set of random variables using a directed acyclic graphical structure. In this paper, we consider a road accident data set collected at one of the popular highways in Iran. Implementing the well-known parents and children algorithm, as a constraint-based approach, we construct a BN model for the available accident data. Once the structure of the BN is learnt, we concentrate on the parameter-learning task. We compute the maximum-likelihood estimates of some parameters of interest, specifically, conditional probability of fatalities in the network.

Language: en

Keywords

Bayesian networks; Directed acyclic graph; Maximum-likelihood estimation; PC algorithm; Road accident data; Structure learning