Realising advanced risk-based port state control inspection using data-driven Bayesian networks

In the past decades, maritime transportation not only contributes to economic prosperity, but also renders many threats to the industry, causing huge casualties and losses. As a result, various maritime safety measures have been developed, including Port State Control (PSC) inspections. In this paper, we propose a data-driven Bayesian Network (BN) based approach to analyse risk factors influencing PSC inspections, and predict the probability of vessel detention. To do so, inspection data of bulk carriers in seven major European countries from 2005 to 2008 1 in Paris MoU is collected to identify the relevant risk factors. Meanwhile, the network structure is constructed via TAN learning and subsequently validated by sensitivity analysis. The results reveal two conclusions: first, the key risk factors influencing PSC inspections include number of deficiencies, type of inspection, Recognised Organisation (RO) and vessel age. Second, the model exploits a novel way to predict the detention probabilities under different situations, which effectively help port authorities to rationalise their inspection regulations as well as allocation of the resources. Further effort will be made to conduct contrastive analysis between ‘Pre-NIR’ period and ‘Post-NIR’ period to test the impact of NIR started in 2008. © 2018 Elsevier Lt

Particle Swarm Optimisation for learning Bayesian Networks

This paper discusses the potential of Particle Swarm Optimisation (PSO) for inducing Bayesian Networks (BNs). Specifically, we detail two methods which adopt the search and score approach to BN learning. The two algorithms are similar in that they both use PSO as the search algorithm, and the K2 metric to score the resulting network. The difference lies in the way networks are constructed. The CONstruct And Repair (CONAR) algorithm generates structures, validates, and repairs if required, and the REstricted STructure (REST) algorithm, only permits valid structures to be developed. Initial experiments indicate that these approaches produce promising results when compared to other BN learning strategies

Modeling associations between genetic markers using Bayesian networks

Motivation: Understanding the patterns of association between polymorphisms at different loci in a population (linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging

Modeling associations between genetic markers using Bayesian networks

Motivation: Understanding the patterns of association between polymorphisms at different loci in a population (linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging

BAYESIAN APPROACH TO THE PROCESS OF IDENTIFICATION OF THE DETERMINANTS OF INNOVATIVENESS

Bayesian belief networks are applied in determining the most important factors of the innovativeness level of national economies. The paper is divided into two parts. The first presentsthe basic theory of Bayesian networks whereas in the second, the belief networks have been generated by an inhouse developed computer system called BeliefSEEKER which was implemented to generate the determinants influencing the innovativeness level of national economies.Qualitative analysis of the generated belief networks provided a way to define a set of the most important dimensions influencing the innovativeness level of economies and then the indicators that form these dimensions. It has been proven that Bayesian networks are very effective methods for multidimensional analysis and forming conclusions and recommendations regarding the strength of each innovative determinant influencing the overall performance of a country’s economy

Línea formativa de inteligencia artificial en la facultad de informática de la UPV-EHU

Con objeto de ampliar la capacitación de sus licenciados en el área de IA ésta facultad ofrece una línea de optatividad que reúne 37 créditos en 7 asignaturas fundamentales. Estas asignaturas se centran en torno a: Sistemas Basados en el Conocimiento, Procesamiento del Lenguaje Natural, Métodos Probabilísticos de la IA, Redes Neuronales e Inferencia Estadística

A Genetic Algorithm for Learning Parameters in Bayesian Networks using Expectation Maximization

Abstract Expectation maximization (EM) is a popular algorithm for parameter estimation in situations with incomplete data. The EM algorithm has, despite its popularity, the disadvantage of often converging to local but non-global optima. Several techniques have been proposed to address this problem, for example initializing EM from multiple random starting points and then selecting the run with the highest likelihood. Unfortunately, this method is computationally expensive. In this paper, our goal is to reduce computational cost while at the same time maximizing likelihood. We propose a Genetic Algorithm for Expectation Maximization (GAEM) for learning parameters in Bayesian networks. GAEM combines the global search property of a genetic algorithm with the local search property of EM. We prove GAEM's global convergence theoretically. Experimentally, we show that GAEM provides significant speed-ups since it tends to select more fit individuals, which converge faster, as parents for the next generation. Specifically, GAEM converges 1.5 to 7 times faster while producing better log-likelihood scores than the traditional EM algorithm