scholarly journals Setting the Port Planning Parameters In Container Terminals through Bayesian Networks

2015 ◽  
Vol 27 (5) ◽  
pp. 395-403 ◽  
Author(s):  
Tomás Rodríguez García ◽  
Nicoletta González Cancelas ◽  
Francisco Soler-Flores
Keyword(s):  
Data Analysis ◽  
Bayesian Networks ◽  
Graphical Models ◽  
Simulation Models ◽  
Probabilistic Graphical Models ◽  
Resource Planning ◽  
Container Terminals ◽  
Correct Prediction ◽  
Port Planning ◽  
Transport Logistics

The correct prediction in the transport logistics has vital importance in the adequate means and resource planning and in their optimisation. Up to this date, port planning studies were based mainly on empirical, analytical or simulation models. This paper deals with the possible use of Bayesian networks in port planning. The methodology indicates the work scenario and how the network was built. The network was afterwards used in container terminals planning, with the support provided by the tools of the Elvira code. The main variables were defined and virtual scenarios inferences were realised in order to carry out the analysis of the container terminals scenarios through probabilistic graphical models. Having performed the data analysis on the different terminals and on the considered variables (berth, area, TEU, crane number), the results show the possible relationships between them. Finally, the conclusions show the obtained values on each considered scenario.

Reconciling internal and external satisfaction through probabilistic graphical models

2017 ◽  
Vol 9 (3/4) ◽  
pp. 347-370 ◽  
Author(s):  
Flaminia Musella ◽  
Roberta Guglielmetti Mugion ◽  
Hendry Raharjo ◽  
Laura Di Pietro
Keyword(s):  
Customer Satisfaction ◽  
Bayesian Networks ◽  
Graphical Models ◽  
Object Oriented ◽  
Holistic Approach ◽  
Probabilistic Graphical Models ◽  
Model Parameters ◽  
Holistic View ◽  
Content Type ◽  
External Customer

Purpose This paper aims to holistically reconcile internal and external customer satisfaction using probabilistic graphical models. The models are useful not only in the identification of the most sensitive factors for the creation of both internal and external customer satisfaction but also in the generation of improvement scenarios in a probabilistic way. Design/methodology/approach Standard Bayesian networks and object-oriented Bayesian networks are used to build probabilistic graphical models for internal and external customers. For each ward, the model is used to evaluate satisfaction drivers by category, and scenarios for the improvement of overall satisfaction variables are developed. A global model that is based on an object-oriented network is modularly built to provide a holistic view of internal and external satisfaction. The linkage is created by building a global index of internal and external satisfaction based on a linear combination. The model parameters are derived from survey data from an Italian hospital. Findings The results that were achieved with the Bayesian networks are consistent with the results of previous research, and they were obtained by using a partial least squares path modelling tool. The variable ‘Experience’ is the most relevant internal factor for the improvement of overall patient satisfaction. To improve overall employee satisfaction, the variable ‘Product/service results’ is the most important. Finally, for a given target of overall internal and external satisfaction, external satisfaction is more sensitive to improvement than internal satisfaction. Originality/value The novelty of the paper lies in the efforts to link internal and external satisfaction based on a probabilistic expert system that can generate improvement scenarios. From an academic viewpoint, this study moves the service profit chain theory (Heskett et al., 1994) forward by delivering operational guidelines for jointly managing the factors that affect internal and external customer satisfaction in service organizations using a holistic approach.

scholarly journals Indirect Causes in Dynamic Bayesian Networks Revisited

2017 ◽  
Vol 59 ◽  
pp. 1-58
Author(s):  
Alexander Motzek ◽  
Ralf Möller
Keyword(s):  
Bayesian Networks ◽  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
Dynamic Bayesian Networks ◽  
Coarse Grained ◽  
Mathematical Basis ◽  
Use Of Time ◽  
Modeling Uncertainties ◽  
Spurious Results ◽  
Over Time

Modeling causal dependencies often demands cycles at a coarse-grained temporal scale. If Bayesian networks are to be used for modeling uncertainties, cycles are eliminated with dynamic Bayesian networks, spreading indirect dependencies over time and enforcing an infinitesimal resolution of time. Without a ``causal design,'' i.e., without anticipating indirect influences appropriately in time, we argue that such networks return spurious results. By identifying activator random variables, we propose activator dynamic Bayesian networks (ADBNs) which are able to rapidly adapt to contexts under a causal use of time, anticipating indirect influences on a solid mathematical basis using familiar Bayesian network semantics. ADBNs are well-defined dynamic probabilistic graphical models allowing one to model cyclic dependencies from local and causal perspectives while preserving a classical, familiar calculus and classically known algorithms, without introducing any overhead in modeling or inference.

scholarly journals Operations for Learning with Graphical Models

1994 ◽  
Vol 2 ◽  
pp. 159-225 ◽  
Author(s):  
W. L. Buntine
Keyword(s):  
Data Analysis ◽  
Bayesian Networks ◽  
Graphical Models ◽  
Exponential Family ◽  
Graphical Model ◽  
Directed Graphs ◽  
Probability Models ◽  
Empirical Learning ◽  
Markov Field ◽  
Regression Techniques

This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, andthe manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximizationalgorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. The main original contributions here are the decompositiontechniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.

Bayesian Networks in the Health Domain

2010 ◽  
pp. 342-376 ◽  
Author(s):  
Shyamala G. Nadathur
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Bayesian Networks ◽  
Network Model ◽  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
Large Datasets ◽  
Statistical Dependence ◽  
Health Domain ◽  
New Methods

Large datasets are regularly collected in biomedicine and healthcare (here referred to as the ‘health domain’). These datasets have some unique characteristics and problems. Therefore there is a need for methods which allow modelling in spite of the uniqueness of the datasets, capable of dealing with missing data, allow integrating data from various sources, explicitly indicate statistical dependence and independence and allow modelling with uncertainties. These requirements have given rise to an influx of new methods, especially from the fields of machine learning and probabilistic graphical models. In particular, Bayesian Networks (BNs), which are a type of graphical network model with directed links that offer a general and versatile approach to capturing and reasoning with uncertainty. In this chapter some background mathematics/statistics, description and relevant aspects of building the networks are given to better understand s and appreciate BN’s potential. There are also brief discussions of their applications, the unique value and the challenges of this modelling technique for the domain. As will be seen in this chapter, with the additional advantages the BNs can offer, it is not surprising that it is becoming an increasingly popular modelling tool in the health domain.

scholarly journals Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis

2020 ◽  
Vol 144 ◽  
pp. 107500 ◽  
Author(s):  
Ming Bo Cai ◽  
Michael Shvartsman ◽  
Anqi Wu ◽  
Hejia Zhang ◽  
Xia Zhu
Keyword(s):  
Data Analysis ◽  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
Fmri Data ◽  
Fmri Data Analysis

Introduction to Bayesian Networks and Influence Diagrams

2012 ◽  
pp. 9-32
Author(s):  
Luis Enrique Sucar
Keyword(s):  
Probability Theory ◽  
Bayesian Networks ◽  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
Dynamic Bayesian Networks ◽  
Influence Diagrams ◽  
General Description ◽  
Sequential Decision ◽  
The Core ◽  
Inference Techniques

In this chapter we will cover the fundamentals of probabilistic graphical models, in particular Bayesian networks and influence diagrams, which are the basis for some of the techniques and applications that are described in the rest of the book. First we will give a general introduction to probabilistic graphical models, including the motivation for using these models, and a brief history and general description of the main types of models. We will also include a brief review of the basis of probability theory. The core of the chapter will be the next three sections devoted to: (i) Bayesian networks, (ii) Dynamic Bayesian networks and (iii) Influence diagrams. For each we will introduce the models, their properties and give some examples. We will briefly describe the main inference techniques for the three types of models. For Bayesian and dynamic Bayesian nets we will talk about learning, including structure and parameter learning, describing the main types of approaches. At the end of the section on influence diagrams we will briefly introduce sequential decision problems as a link to the chapter on MDPs and POMDPs. We conclude the chapter with a summary and pointers for further reading for each topic.

Probabalistic Inference for Actor Centered Models

2008 ◽  
pp. 115-125
Author(s):  
Gero Schwenk
Keyword(s):  
Social Science ◽  
Bayesian Networks ◽  
Graphical Models ◽  
Empirical Data ◽  
Probabilistic Graphical Models ◽  
Probabalistic Inference ◽  
Different Levels ◽  
Area Of Application

The analysis of relations between different levels of a system is a key issue in social science simulation. Here, I discuss the contribution of different modeling methodologies to this. Special emphasis is given to the formalism of “Probabilistic Graphical Models“, resp. “Bayesian Networks“, which is both advantageous for level transitory inference and integration of empirical data. Furthermore, issues of practicability and area of application are considered. The argumentation is exemplified by demonstration of a toy-application for which explicit level-transitory statements are inferred.

THE HUGIN TOOL FOR PROBABILISTIC GRAPHICAL MODELS

2005 ◽  
Vol 14 (03) ◽  
pp. 507-543 ◽  
Author(s):  
ANDERS L. MADSEN ◽  
FRANK JENSEN ◽  
UFFE B. KJÆRULFF ◽  
MICHAEL LANG
Keyword(s):  
Knowledge Representation ◽  
Bayesian Networks ◽  
Empirical Analysis ◽  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
General Purpose ◽  
Influence Diagrams ◽  
Learning Tasks

As the framework of probabilistic graphical models becomes increasingly popular for knowledge representation and inference, the need for efficient tools for its support is increasing. The Hugin Tool is a general purpose tool for construction, maintenance, and deployment of Bayesian networks and influence diagrams. This paper surveys the key functionality of the Hugin Tool and reports on new advances of the tool. Furthermore, an empirical analysis reports on the efficiency of the Hugin Tool on common inference and learning tasks.

Retrieving Medical Records Using Bayesian Networks

2011 ◽  
pp. 960-964
Author(s):  
Luis M. de Campos ◽  
Juan M. Fernandez-Luna ◽  
Juan F. Huete
Keyword(s):  
Information Retrieval ◽  
Bayesian Networks ◽  
Graphical Models ◽  
Medical Records ◽  
Probabilistic Graphical Models ◽  
Wide Range ◽  
Textual Representation

Bayesian networks (Jensen, 2001) are powerful tools for dealing with uncertainty. They have been successfully applied in a wide range of domains where this property is an important feature, as in the case of information retrieval (IR) (Turtle & Croft, 1991). This field (Baeza-Yates & Ribeiro-Neto, 1999) is concerned with the representation, storage, organization, and accessing of information items (the textual representation of any kind of object). Uncertainty is also present in this field, and, consequently, several approaches based on these probabilistic graphical models have been designed in an attempt to represent documents and their contents (expressed by means of indexed terms), and the relationships between them, so as to retrieve as many relevant documents as possible, given a query submitted by a user.

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