TY  - JOUR
PY  - 2017//
TI  - Road accident data analysis using Bayesian networks
JO  - Transportation letters
A1  - Karimnezhad, Ali
A1  - Moradi, Fahimeh
SP  - 12
EP  - 19
VL  - 9
IS  - 1
N2  - 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>
LA  - en
SN  - 1942-7867
UR  - http://dx.doi.org/10.1080/19427867.2015.1131960
ID  - ref1
ER  -