@article{ref1,
title="Road accident data analysis using Bayesian networks",
journal="Transportation letters",
year="2017",
author="Karimnezhad, Ali and Moradi, Fahimeh",
volume="9",
number="1",
pages="12-19",
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.<p /> <p>Language: en</p>",
language="en",
issn="1942-7867",
doi="10.1080/19427867.2015.1131960",
url="http://dx.doi.org/10.1080/19427867.2015.1131960"
}