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Bayesian Networks (bayesian + network)
Selected AbstractsTowards a decision support system for health promotion in nursingJOURNAL OF ADVANCED NURSING, Issue 2 2003Kate Caelli PhD RN RM Aims. This study was designed to investigate what type of models, techniques and data are necessary to support the development of a decision support system for health promotion practice in nursing. Specifically, the research explored how interview data can be interpreted in terms of Concept Networks and Bayesian Networks, both of which provide formal methods for describing the dependencies between factors or variables in the context of decision-making in health promotion. Background. In nursing, the lack of generally accepted examples or guidelines by which to implement or evaluate health promotion practice is a challenge. Major gaps have been identified between health promotion rhetoric and practice and there is a need for health promotion to be presented in ways that make its attitudes and practices more easily understood. New tools, paradigms and techniques to encourage the practice of health promotion would appear to be beneficial. Concept Networks and Bayesian Networks are techniques that may assist the research team to understand and explicate health promotion more specifically and formally than has been the case, so that it may more readily be integrated into nursing practice. Methods. As the ultimate goal of the study was to investigate ways to use the techniques described above, it was necessary to first generate data as text. Textual descriptions of health promotion in nursing were derived from in-depth qualitative interviews with nurses nominated by their peers as expert health promoting practitioners. Findings. The nurses in this study gave only general and somewhat vague outlines of the concepts and ideas that guided their practice. These data were compared with descriptions from various sources that describe health promotion practices in nursing, then examples of a Conceptual Network and a representative Bayesian Network were derived from the data. Conclusions. The study highlighted the difficulty in describing health promotion practice, even among nurses recognized for their expertise in health promotion. Nevertheless, it indicated the data collection and analysis methods necessary to explicate the cognitive processes of health promotion and highlighted the benefits of using formal conceptualization techniques to improve health promotion practice. [source] Traffic Estimation and Optimal Counting Location Without Path Enumeration Using Bayesian NetworksCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 3 2008Enrique Castillo A combination (bi-level) of an OD-pair matrix estimation model based on Bayesian networks, and a Wardrop-minimum-variance model, which identifies origins and destinations of link flows, is used to estimate OD-pair and unobserved link flows based on some observations of links and/or OD-pair flows. The Bayesian network model is also used to select the optimal number and locations of the links counters based on maximum correlation. Finally, the proposed methods are illustrated by their application to the Nguyen,Dupuis and the Ciudad Real networks. [source] Bayesian Networks and Adaptive Management of Wildlife HabitatCONSERVATION BIOLOGY, Issue 4 2010ALISON L. HOWES herramientas para la toma de decisiones; incertidumbre ecológica; pastoreo feral; regímenes de quema; validación de modelos Abstract:,Adaptive management is an iterative process of gathering new knowledge regarding a system's behavior and monitoring the ecological consequences of management actions to improve management decisions. Although the concept originated in the 1970s, it is rarely actively incorporated into ecological restoration. Bayesian networks (BNs) are emerging as efficient ecological decision-support tools well suited to adaptive management, but examples of their application in this capacity are few. We developed a BN within an adaptive-management framework that focuses on managing the effects of feral grazing and prescribed burning regimes on avian diversity within woodlands of subtropical eastern Australia. We constructed the BN with baseline data to predict bird abundance as a function of habitat structure, grazing pressure, and prescribed burning. Results of sensitivity analyses suggested that grazing pressure increased the abundance of aggressive honeyeaters, which in turn had a strong negative effect on small passerines. Management interventions to reduce pressure of feral grazing and prescribed burning were then conducted, after which we collected a second set of field data to test the response of small passerines to these measures. We used these data, which incorporated ecological changes that may have resulted from the management interventions, to validate and update the BN. The network predictions of small passerine abundance under the new habitat and management conditions were very accurate. The updated BN concluded the first iteration of adaptive management and will be used in planning the next round of management interventions. The unique belief-updating feature of BNs provides land managers with the flexibility to predict outcomes and evaluate the effectiveness of management interventions. Resumen:,El manejo adaptativo es un proceso interactivo de recopilación de conocimiento nuevo relacionado con el comportamiento de un sistema y el monitoreo de las consecuencias ecológicas de las acciones de manejo para refinar las opciones de manejo. Aunque el concepto se originó en la década de los 1970s, rara vez es incorporado activamente en la restauración ecológica. Las redes Bayesianas (RBs) están emergiendo como herramientas eficientes para la toma de decisiones ecológicas en el contexto del manejo adaptativo, pero los ejemplos de su aplicación en este sentido son escasos. Desarrollamos una RB en el marco del manejo adaptativo que se centra en el manejo de los efectos del pastoreo feral y los regímenes de quemas prescritas sobre la diversidad de aves en bosques subtropicales del este de Australia. Construimos la RB con datos para predecir la abundancia de aves como una función de la estructura del hábitat, la presión de pastoreo y las quemas prescritas. Los resultados del análisis de sensibilidad sugieren que la presión de pastoreo incrementó la abundancia de melífagos agresivos, que a su vez tuvieron un fuerte efecto negativo sobre paserinos pequeños. Posteriormente se llevaron a cabo intervenciones de manejo para reducir la presión del pastoreo feral y quemas prescritas, después de las cuales recolectamos un segundo conjunto de datos de campo para probar la respuesta de paserinos pequeños a estas medidas. Utilizamos estos datos, que incorporaron cambios ecológicos que pueden haber resultado de la intervención de manejo, para validar y actualizar la RB. Las predicciones de la abundancia de paserinos pequeños bajo las nuevas condiciones de hábitat y manejo fueron muy precisas. La RB actualizada concluyó la primera iteración de manejo adaptativo y será utilizada para la planificación de la siguiente ronda de intervenciones de manejo. La característica única de actualización de la RBs permite que los manejadores tengan flexibilidad para predecir los resultados y evaluar la efectividad de las intervenciones de manejo. [source] Discrete dynamic Bayesian network analysis of fMRI dataHUMAN BRAIN MAPPING, Issue 1 2009John Burge Abstract We examine the efficacy of using discrete Dynamic Bayesian Networks (dDBNs), a data-driven modeling technique employed in machine learning, to identify functional correlations among neuroanatomical regions of interest. Unlike many neuroimaging analysis techniques, this method is not limited by linear and/or Gaussian noise assumptions. It achieves this by modeling the time series of neuroanatomical regions as discrete, as opposed to continuous, random variables with multinomial distributions. We demonstrated this method using an fMRI dataset collected from healthy and demented elderly subjects (Buckner, et al., 2000: J Cogn Neurosci 12:24-34) and identify correlates based on a diagnosis of dementia. The results are validated in three ways. First, the elicited correlates are shown to be robust over leave-one-out cross-validation and, via a Fourier bootstrapping method, that they were not likely due to random chance. Second, the dDBNs identified correlates that would be expected given the experimental paradigm. Third, the dDBN's ability to predict dementia is competitive with two commonly employed machine-learning classifiers: the support vector machine and the Gaussian naïve Bayesian network. We also verify that the dDBN selects correlates based on non-linear criteria. Finally, we provide a brief analysis of the correlates elicited from Buckner et al.'s data that suggests that demented elderly subjects have reduced involvement of entorhinal and occipital cortex and greater involvement of the parietal lobe and amygdala in brain activity compared with healthy elderly (as measured via functional correlations among BOLD measurements). Limitations and extensions to the dDBN method are discussed. Hum Brain Mapp, 2009. © 2007 Wiley-Liss, Inc. [source] Towards a decision support system for health promotion in nursingJOURNAL OF ADVANCED NURSING, Issue 2 2003Kate Caelli PhD RN RM Aims. This study was designed to investigate what type of models, techniques and data are necessary to support the development of a decision support system for health promotion practice in nursing. Specifically, the research explored how interview data can be interpreted in terms of Concept Networks and Bayesian Networks, both of which provide formal methods for describing the dependencies between factors or variables in the context of decision-making in health promotion. Background. In nursing, the lack of generally accepted examples or guidelines by which to implement or evaluate health promotion practice is a challenge. Major gaps have been identified between health promotion rhetoric and practice and there is a need for health promotion to be presented in ways that make its attitudes and practices more easily understood. New tools, paradigms and techniques to encourage the practice of health promotion would appear to be beneficial. Concept Networks and Bayesian Networks are techniques that may assist the research team to understand and explicate health promotion more specifically and formally than has been the case, so that it may more readily be integrated into nursing practice. Methods. As the ultimate goal of the study was to investigate ways to use the techniques described above, it was necessary to first generate data as text. Textual descriptions of health promotion in nursing were derived from in-depth qualitative interviews with nurses nominated by their peers as expert health promoting practitioners. Findings. The nurses in this study gave only general and somewhat vague outlines of the concepts and ideas that guided their practice. These data were compared with descriptions from various sources that describe health promotion practices in nursing, then examples of a Conceptual Network and a representative Bayesian Network were derived from the data. Conclusions. The study highlighted the difficulty in describing health promotion practice, even among nurses recognized for their expertise in health promotion. Nevertheless, it indicated the data collection and analysis methods necessary to explicate the cognitive processes of health promotion and highlighted the benefits of using formal conceptualization techniques to improve health promotion practice. [source] Resolving Paternity Relationships Using X-Chromosome STRs and Bayesian NetworksJOURNAL OF FORENSIC SCIENCES, Issue 4 2007Didier Hatsch Ph.D. Abstract:, X-chromosomal short tandem repeats (X-STRs) are very useful in complex paternity cases because they are inherited by male and female offspring in different ways. They complement autosomal STRs (as-STRs) allowing higher paternity probabilities to be attained. These probabilities are expressed in a likelihood ratio (LR). The formulae needed to calculate LR depend on the genotype combinations of suspected pedigrees. LR can also be obtained by the use of Bayesian networks (BNs). These are graphical representations of real situations that can be used to easily calculate complex probabilities. In the present work, two BNs are presented, which are designed to derive LRs for half-sisters/half-sisters and mother/daughter/paternal grandmother relationships. These networks were validated against known formulae and show themselves to be useful in other suspect pedigree situations than those for which they were developed. The BNs were applied in two paternity cases. The application of the mother/daughter/paternal grandmother BN highlighted the complementary value of X-STRs to as-STRs. The same case evaluated without the mother underlined that missing information tends to be conservative if the alleged father is the biological father and otherwise nonconservative. The half-sisters case shows a limitation of statistical interpretations in regard to high allelic frequencies. [source] Behavior selection of mobile robot based on integration of multimodal informationELECTRICAL ENGINEERING IN JAPAN, Issue 2 2007Bin Chen Abstract Recently, biologically inspired robots have been developed to acquire the capacity for directing visual attention to salient stimulus generated from the audiovisual environment. For the purpose of realizing this behavior, a general method is to calculate saliency maps to represent how much the external information attracts the robot's visual attention, where the audiovisual information and robot's motion status should be involved. In this paper, we represent a visual attention model where three modalities,audio information, visual information, and robot's motor status,are considered, because previous research has not considered all of them. First, we introduce a 2D density map, on which the value denotes how much the robot pays attention to each spatial location. Then we model the attention density using a Bayesian network where the robot's motion statuses are involved. Next, the information from both audio and visual modalities is integrated with the attention density map in integrate-fire neurons. The robot can direct its attention to the locations where the integrate-fire neurons are fired. Finally, the visual attention model is applied to make the robot select the visual information from the environment, and react to the content selected. Experimental results show that it is possible for robots to acquire the visual information related to their behaviors by using the attention model considering motion statuses. The robot can select its behaviors to adapt to the dynamic environment as well as to switch to another task according to the recognition results of visual attention. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 158(2): 39,48, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20335 [source] User profiling on the Web based on deep knowledge and sequential questioningEXPERT SYSTEMS, Issue 1 2006Silvano Mussi Abstract: User profiling on the Web is a topic that has attracted a great number of technological approaches and applications. In most user profiling approaches the website learns profiles from data implicitly acquired from user behaviours, i.e. observing the behaviours of users with a statistically significant number of accesses. This paper presents an alternative approach. In this approach the website explicitly acquires data from users, user interests are represented in a Bayesian network, and user profiles are enriched and refined over time. The profile enrichment is achieved through a sequential asking algorithm based on the value-of-information theory using the Shannon entropy concept. However, what mostly characterizes the approach is the fact that the user is involved in a collaborative process of profile building. The approach has been tried out for over a year in a real application. On the basis of the experimental results the approach turns out to be particularly suitable for applications where the website is strongly based on deep domain knowledge (as for example is the case for scientific websites) and has a community of users that share the same domain knowledge of the website and produce a ,low' number of accesses (,low' compared to the high number of accesses of a typical commercial website). After presenting the technical aspects of the approach, we discuss the underlying ideas in the light of the experimental results and the literature on human,computer interaction and user profiling. [source] Discrete dynamic Bayesian network analysis of fMRI dataHUMAN BRAIN MAPPING, Issue 1 2009John Burge Abstract We examine the efficacy of using discrete Dynamic Bayesian Networks (dDBNs), a data-driven modeling technique employed in machine learning, to identify functional correlations among neuroanatomical regions of interest. Unlike many neuroimaging analysis techniques, this method is not limited by linear and/or Gaussian noise assumptions. It achieves this by modeling the time series of neuroanatomical regions as discrete, as opposed to continuous, random variables with multinomial distributions. We demonstrated this method using an fMRI dataset collected from healthy and demented elderly subjects (Buckner, et al., 2000: J Cogn Neurosci 12:24-34) and identify correlates based on a diagnosis of dementia. The results are validated in three ways. First, the elicited correlates are shown to be robust over leave-one-out cross-validation and, via a Fourier bootstrapping method, that they were not likely due to random chance. Second, the dDBNs identified correlates that would be expected given the experimental paradigm. Third, the dDBN's ability to predict dementia is competitive with two commonly employed machine-learning classifiers: the support vector machine and the Gaussian naïve Bayesian network. We also verify that the dDBN selects correlates based on non-linear criteria. Finally, we provide a brief analysis of the correlates elicited from Buckner et al.'s data that suggests that demented elderly subjects have reduced involvement of entorhinal and occipital cortex and greater involvement of the parietal lobe and amygdala in brain activity compared with healthy elderly (as measured via functional correlations among BOLD measurements). Limitations and extensions to the dDBN method are discussed. Hum Brain Mapp, 2009. © 2007 Wiley-Liss, Inc. [source] From dynamic influence nets to dynamic Bayesian networks: A transformation algorithmINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 8 2009Sajjad Haider This paper presents an algorithm to transform a dynamic influence net (DIN) into a dynamic Bayesian network (DBN). The transformation aims to bring the best of both probabilistic reasoning paradigms. The advantages of DINs lie in their ability to represent causal and time-varying information in a compact and easy-to-understand manner. They facilitate a system modeler in connecting a set of desired effects and a set of actionable events through a series of dynamically changing cause and effect relationships. The resultant probabilistic model is then used to analyze different courses of action in terms of their effectiveness to achieve the desired effect(s). The major drawback of DINs is their inability to incorporate evidence that arrive during the execution of a course of action (COA). Several belief-updating algorithms, on the other hand, have been developed for DBNs that enable a system modeler to insert evidence in dynamic probabilistic models. Dynamic Bayesian networks, however, suffer from the intractability of knowledge acquisition. The presented transformation algorithm combines the advantages of both DINs and DBNs. It enables a system analyst to capture a complex situation using a DIN and pick the best (or close-to-best) COA that maximizes the likelihood of achieving the desired effect. During the execution, if evidence becomes available, the DIN is converted into an equivalent DBN and beliefs of other nodes in the network are updated. If required, the selected COA can be revised on the basis of the recently received evidence. The presented methodology is applicable in domains requiring strategic level decision making in highly complex situations, such as war games, real-time strategy video games, and business simulation games. © 2009 Wiley Periodicals, Inc. [source] A study of argumentation in a causal probabilistic humanistic domain: Genetic counselingINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 1 2007Nancy Green We present the results of an in-depth qualitative analysis of argumentation in two genetic counseling patient letters. In addition to argumentation techniques designed for medical experts, we found other types of causal argumentation designed for lay readers, reflecting the educational and supportive counseling functions of these letters. Analysis was facilitated by use of a coding scheme for representing causal probabilistic biomedical content of the letters as Bayesian networks. We define the argument techniques used in the letters in terms of Bayesian network, semantic network, argumentation theory, and user model concepts rather than in terms of genetics concepts. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 71,93, 2007. [source] Hybrid Bayesian networks: making the hybrid Bayesian classifier robust to missing training dataJOURNAL OF CHEMOMETRICS, Issue 5 2003Nathaniel A. Woody Abstract Many standard classification methods are incapable of handling missing values in a sample. Instead, these methods must rely on external filling methods in order to estimate the missing values. The hybrid network proposed in this paper is an extension of the hybrid classifier that is robust to missing values. The hybrid network is produced by performing empirical Bayesian network structure learning to create a Bayesian network that retains its classification ability in the presence of missing data in both training and test cases. The performance of the hybrid network is measured by calculating a misclassification rate when data are removed from a dataset. These misclassification curves are then compared against similar curves produced from the hybrid classifier and from a classification tree. Copyright © 2003 John Wiley & Sons, Ltd. [source] Modeling and analysis of disease and risk factors through learning Bayesian networks from observational dataQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 3 2008Jing Li Abstract This paper focuses on identification of the relationships between a disease and its potential risk factors using Bayesian networks in an epidemiologic study, with the emphasis on integrating medical domain knowledge and statistical data analysis. An integrated approach is developed to identify the risk factors associated with patients' occupational histories and is demonstrated using real-world data. This approach includes several steps. First, raw data are preprocessed into a format that is acceptable to the learning algorithms of Bayesian networks. Some important considerations are discussed to address the uniqueness of the data and the challenges of the learning. Second, a Bayesian network is learned from the preprocessed data set by integrating medical domain knowledge and generic learning algorithms. Third, the relationships revealed by the Bayesian network are used for risk factor analysis, including identification of a group of people who share certain common characteristics and have a relatively high probability of developing the disease, and prediction of a person's risk of developing the disease given information on his/her occupational history. Copyright © 2007 John Wiley & Sons, Ltd. [source] Methodology for the optimal component selection of electronic devices under reliability and cost constraintsQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 8 2007E. P. Zafiropoulos Abstract The objective of this paper is to present an efficient computational methodology for the reliability optimization of electronic devices under cost constraints. The system modeling for calculating the reliability indices of the electronic devices is based on Bayesian networks using the fault tree approach, in order to overcome the limitations of the series,parallel topology of the reliability block diagrams. Furthermore, the Bayesian network modeling for the reliability analysis provides greater flexibility for representing multiple failure modes and dependent failure events, and simplifies fault diagnosis and reliability allocation. The optimal selection of components is obtained using the simulated annealing algorithm, which has proved to be highly efficient in complex optimization problems where gradient-based methods can not be applied. The reliability modeling and optimization methodology was implemented into a computer program in Matlab using a Bayesian network toolbox. The methodology was applied for the optimal selection of components for an electrical switch of power installations under reliability and cost constraints. The full enumeration of the solution space was calculated in order to demonstrate the efficiency of the proposed optimization algorithm. The results obtained are excellent since a near optimum solution was found in a small fraction of the time needed for the complete enumeration (3%). All the optimum solutions found during consecutive runs of the optimization algorithm lay in the top 0.3% of the solutions that satisfy the reliability and cost constraints. Copyright © 2007 John Wiley & Sons, Ltd. [source] Traffic Estimation and Optimal Counting Location Without Path Enumeration Using Bayesian NetworksCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 3 2008Enrique Castillo A combination (bi-level) of an OD-pair matrix estimation model based on Bayesian networks, and a Wardrop-minimum-variance model, which identifies origins and destinations of link flows, is used to estimate OD-pair and unobserved link flows based on some observations of links and/or OD-pair flows. The Bayesian network model is also used to select the optimal number and locations of the links counters based on maximum correlation. Finally, the proposed methods are illustrated by their application to the Nguyen,Dupuis and the Ciudad Real networks. [source] Distributed parallel compilation of MSBNsCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 12 2009Xiangdong An Abstract Multiply sectioned Bayesian networks (MSBNs) support multiagent probabilistic inference in distributed large problem domains. Inference with MSBNs can be performed using their compiled representations. The compilation involves moralization and triangulation of a set of local graphical structures. Privacy of agents may prevent us from compiling MSBNs at a central location. In earlier work, agents performed compilation sequentially via a depth-first traversal of the hypertree that organizes local subnets, where communication failure between any two agents would crush the whole work. In this paper, we present an asynchronous compilation method by which multiple agents compile MSBNs in full parallel. Compared with the traversal compilation, the asynchronous one is robust, self-adaptive, and fault-tolerant. Experiments show that both methods provide similar quality compilation to simple MSBNs, but the asynchronous one provides much higher quality compilation to complex MSBNs. Empirical study also indicates that the asynchronous one is consistently faster than the traversal one. Copyright © 2009 John Wiley & Sons, Ltd. [source] Bayesian Networks and Adaptive Management of Wildlife HabitatCONSERVATION BIOLOGY, Issue 4 2010ALISON L. HOWES herramientas para la toma de decisiones; incertidumbre ecológica; pastoreo feral; regímenes de quema; validación de modelos Abstract:,Adaptive management is an iterative process of gathering new knowledge regarding a system's behavior and monitoring the ecological consequences of management actions to improve management decisions. Although the concept originated in the 1970s, it is rarely actively incorporated into ecological restoration. Bayesian networks (BNs) are emerging as efficient ecological decision-support tools well suited to adaptive management, but examples of their application in this capacity are few. We developed a BN within an adaptive-management framework that focuses on managing the effects of feral grazing and prescribed burning regimes on avian diversity within woodlands of subtropical eastern Australia. We constructed the BN with baseline data to predict bird abundance as a function of habitat structure, grazing pressure, and prescribed burning. Results of sensitivity analyses suggested that grazing pressure increased the abundance of aggressive honeyeaters, which in turn had a strong negative effect on small passerines. Management interventions to reduce pressure of feral grazing and prescribed burning were then conducted, after which we collected a second set of field data to test the response of small passerines to these measures. We used these data, which incorporated ecological changes that may have resulted from the management interventions, to validate and update the BN. The network predictions of small passerine abundance under the new habitat and management conditions were very accurate. The updated BN concluded the first iteration of adaptive management and will be used in planning the next round of management interventions. The unique belief-updating feature of BNs provides land managers with the flexibility to predict outcomes and evaluate the effectiveness of management interventions. Resumen:,El manejo adaptativo es un proceso interactivo de recopilación de conocimiento nuevo relacionado con el comportamiento de un sistema y el monitoreo de las consecuencias ecológicas de las acciones de manejo para refinar las opciones de manejo. Aunque el concepto se originó en la década de los 1970s, rara vez es incorporado activamente en la restauración ecológica. Las redes Bayesianas (RBs) están emergiendo como herramientas eficientes para la toma de decisiones ecológicas en el contexto del manejo adaptativo, pero los ejemplos de su aplicación en este sentido son escasos. Desarrollamos una RB en el marco del manejo adaptativo que se centra en el manejo de los efectos del pastoreo feral y los regímenes de quemas prescritas sobre la diversidad de aves en bosques subtropicales del este de Australia. Construimos la RB con datos para predecir la abundancia de aves como una función de la estructura del hábitat, la presión de pastoreo y las quemas prescritas. Los resultados del análisis de sensibilidad sugieren que la presión de pastoreo incrementó la abundancia de melífagos agresivos, que a su vez tuvieron un fuerte efecto negativo sobre paserinos pequeños. Posteriormente se llevaron a cabo intervenciones de manejo para reducir la presión del pastoreo feral y quemas prescritas, después de las cuales recolectamos un segundo conjunto de datos de campo para probar la respuesta de paserinos pequeños a estas medidas. Utilizamos estos datos, que incorporaron cambios ecológicos que pueden haber resultado de la intervención de manejo, para validar y actualizar la RB. Las predicciones de la abundancia de paserinos pequeños bajo las nuevas condiciones de hábitat y manejo fueron muy precisas. La RB actualizada concluyó la primera iteración de manejo adaptativo y será utilizada para la planificación de la siguiente ronda de intervenciones de manejo. La característica única de actualización de la RBs permite que los manejadores tengan flexibilidad para predecir los resultados y evaluar la efectividad de las intervenciones de manejo. [source] Sequential decision-theoretic models and expert systemsEXPERT SYSTEMS, Issue 2 2002Silvano Mussi Sequential decision models are an important component of expert systems since, in general, the cost of acquiring information is significant and there is a trade-off between the cost and the value of information. Many expert systems in various domains (business, engineering, medicine etc.), needing costly inputs that are not known until the system operates, have to face this problem. In the last decade the field of sequential decision models based on decision theory (sequential decision-theoretic models) have become more and more important due to both the continuous progress made by research in Bayesian networks and the availability of modern powerful tools for building Bayesian networks and for probability propagation. This paper provides readers (especially knowledge engineers and expert system designers) with a unified and integrated presentation of the disparate literature in the field of sequential decision-making based on decision theory, in order to improve comprehensibility and accessibility. Besides the presentation of the general theory, a view of sequential diagnosis as an instance of the general concept of sequential decision-theoretic models is also shown. [source] From dynamic influence nets to dynamic Bayesian networks: A transformation algorithmINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 8 2009Sajjad Haider This paper presents an algorithm to transform a dynamic influence net (DIN) into a dynamic Bayesian network (DBN). The transformation aims to bring the best of both probabilistic reasoning paradigms. The advantages of DINs lie in their ability to represent causal and time-varying information in a compact and easy-to-understand manner. They facilitate a system modeler in connecting a set of desired effects and a set of actionable events through a series of dynamically changing cause and effect relationships. The resultant probabilistic model is then used to analyze different courses of action in terms of their effectiveness to achieve the desired effect(s). The major drawback of DINs is their inability to incorporate evidence that arrive during the execution of a course of action (COA). Several belief-updating algorithms, on the other hand, have been developed for DBNs that enable a system modeler to insert evidence in dynamic probabilistic models. Dynamic Bayesian networks, however, suffer from the intractability of knowledge acquisition. The presented transformation algorithm combines the advantages of both DINs and DBNs. It enables a system analyst to capture a complex situation using a DIN and pick the best (or close-to-best) COA that maximizes the likelihood of achieving the desired effect. During the execution, if evidence becomes available, the DIN is converted into an equivalent DBN and beliefs of other nodes in the network are updated. If required, the selected COA can be revised on the basis of the recently received evidence. The presented methodology is applicable in domains requiring strategic level decision making in highly complex situations, such as war games, real-time strategy video games, and business simulation games. © 2009 Wiley Periodicals, Inc. [source] Effective database processing for classification and regression with continuous variablesINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 12 2007E. Di Tomaso This article proposes a method for manipulating a database of instances relative to discrete and continuous variables. A fuzzy partition is used to discretize continuous domains. A reorganized form of representing a relational database is proposed. The new form of representation is called an effective database. The effective database is tested on classification and regression problems using general Bayesian networks and Näive Bayes classifiers. The structures and the parameters of the classifiers are estimated from the effective database. An algorithm for updating with soft evidence is used to test the induced models, when continuous variables are present. The experiments show that the effective database procedure produces a selection of relevant information from data, which improves in some cases the prediction accuracy of the classifiers. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1271,1285, 2007. [source] A study of argumentation in a causal probabilistic humanistic domain: Genetic counselingINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 1 2007Nancy Green We present the results of an in-depth qualitative analysis of argumentation in two genetic counseling patient letters. In addition to argumentation techniques designed for medical experts, we found other types of causal argumentation designed for lay readers, reflecting the educational and supportive counseling functions of these letters. Analysis was facilitated by use of a coding scheme for representing causal probabilistic biomedical content of the letters as Bayesian networks. We define the argument techniques used in the letters in terms of Bayesian network, semantic network, argumentation theory, and user model concepts rather than in terms of genetics concepts. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 71,93, 2007. [source] An extension of the differential approach for Bayesian network inference to dynamic Bayesian networksINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 8 2004Boris Brandherm We extend Darwiche's differential approach to inference in Bayesian networks (BNs) to handle specific problems that arise in the context of dynamic Bayesian networks (DBNs). We first summarize Darwiche's approach for BNs, which involves the representation of a BN in terms of a multivariate polynomial. We then show how procedures for the computation of corresponding polynomials for DBNs can be derived. These procedures permit not only an exact roll-up of old time slices but also a constant-space evaluation of DBNs. The method is applicable to both forward and backward propagation, and it does not presuppose that each time slice of the DBN has the same structure. It is compatible with approximative methods for roll-up and evaluation of DBNs. Finally, we discuss further ways of improving efficiency, referring as an example to a mobile system in which the computation is distributed over a normal workstation and a resource-limited mobile device. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 727,748, 2004. [source] Evolutionary learning of dynamic probabilistic models with large time lagsINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 5 2001Allan Tucker In this paper, we explore the automatic explanation of multivariate time series (MTS) through learning dynamic Bayesian networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for static Bayesian networks but are adapted for DBNs in this paper. These are extensively tested on both synthetic and real-world MTS for various aspects of efficiency and accuracy. By proposing a simple representation scheme, an efficient learning methodology, and several useful heuristics, we have found that the proposed method is more efficient for learning DBNs from MTS with large time lags, especially in time-demanding situations. © 2001 John Wiley & Sons, Inc. [source] Interpreting DNA Evidence: A ReviewINTERNATIONAL STATISTICAL REVIEW, Issue 3 2003L.A. Foreman Summary The paper provides a review of current issues relating to the use of DNA profiling in forensic science. A short historical section gives the main statistical milestones that occurred during a rapid development of DNA technology and operational uses. Greater detail is then provided for interpretation issues involving STR DNA profiles, including: , methods that take account of population substructure in DNA calculations; , parallel work carried out by the US National Research Council; , the move away from multiple independence testing in favour of experiments that demonstrate the robustness of casework procedures; , the questionable practice of source attribution ,with reasonable scientific certainty'; , the effect on the interpretation of profiles obtained under increasingly sensitive techniques, the LCN technique in particular; , the use of DNA profiles as an intelligence tool; , the interpretation of DNA mixtures. Experience of presenting DNA evidence within UK courts is also discussed. The paper then summarises a generic interpretation framework based on the concept of likelihood ratio within a hierarchy of propositions. Finally the use of Bayesian networks to interpret DNA evidence is reviewed. Résumé Cet article présente un inventaire des questions relativesá l'utilisation du profilage ADN dans la science légale. Une courte section historique décrit les principales étapes statistiques qui ont eu lieu pendant le rapide développement de la technologie ADN et ses utilisations opérationnelles. De plus grands détails sont ensuite donnés pour l'interprétation de questions sur les profils AND STR, ce qui inclut: ,les méthodes qui tiennent compte des sous-structures de population dans les calculs ADN; ,le travail conduit en paralléle par le Conseil de Recherche Nationale des Etats-Unis (NRC); ,l'évolution depuis les tests d'indépendance multiple vers des expériences qui démontrent la robustesse des procédures; ,la pratique contestable de l'attribution de source avec "certitude scientifique raisonnable"; ,l'effet de l'interprétation des profils obtenus sous techniques de plus en plus sensibles, la technique LCN en particulier ,l'utilisation de profils ADN comme outil d'intelligence; ,l'interprétation de mélanges ADN. L'expérience de ce type de preuve dans les tribunaux britanniques sera également présentée et commentée. L'article présentera un cavenas d'interprétation centré sur le concept de rapport de vraisemblance, inscrit dans une hérarchie de propositions. Finalement, l'utilisation de réseaux Bayesien pour interpréter la preuve par ADN sera abordée. [source] Evaluation of the evidential value of physicochemical data by a Bayesian network approachJOURNAL OF CHEMOMETRICS, Issue 7-8 2010Grzegorz Zadora Abstract The growing interest in applications of Bayesian networks (BNs) in forensic science raises the question of whether BN could be used in forensic practice for the evaluation of results from physicochemical analysis of a limited number of observations from flammable liquids (weathered kerosene and diesel fuel) by automated thermal desorption gas chromatography mass spectrometry (ATD-GC/MS), car paints by pyrolysis gas chromatography mass spectrometry (Py-GC/MS) and fibres by microspectrophotometry (MSP) in the visible (VIS) range. Therefore, various simple BN models, which allow the evaluation of both discrete and continuous types of data, were studied in order to address questions raised by the representatives of the administration of justice, concerning the identification and classification of objects into certain categories and/or the association between two items. The results of the evaluation performed by BN models were expressed in the form of a likelihood ratio, which is a well-documented measure of evidential value in the forensic field. From the results obtained, it can be concluded that BN models seem to be promising tool for evaluating physicochemical data. Copyright © 2010 John Wiley & Sons, Ltd. [source] Hybrid Bayesian networks: making the hybrid Bayesian classifier robust to missing training dataJOURNAL OF CHEMOMETRICS, Issue 5 2003Nathaniel A. Woody Abstract Many standard classification methods are incapable of handling missing values in a sample. Instead, these methods must rely on external filling methods in order to estimate the missing values. The hybrid network proposed in this paper is an extension of the hybrid classifier that is robust to missing values. The hybrid network is produced by performing empirical Bayesian network structure learning to create a Bayesian network that retains its classification ability in the presence of missing data in both training and test cases. The performance of the hybrid network is measured by calculating a misclassification rate when data are removed from a dataset. These misclassification curves are then compared against similar curves produced from the hybrid classifier and from a classification tree. Copyright © 2003 John Wiley & Sons, Ltd. [source] An enhanced Bayesian model to detect students' learning styles in Web-based coursesJOURNAL OF COMPUTER ASSISTED LEARNING, Issue 4 2008P. García Abstract Students acquire and process information in different ways depending on their learning styles. To be effective, Web-based courses should guarantee that all the students learn despite their different learning styles. To achieve this goal, we have to detect how students learn: reflecting or acting; steadily or in fits and starts; intuitively or sensitively. In a previous work, we have presented an approach that uses Bayesian networks to detect a student's learning style in Web-based courses. In this work, we present an enhanced Bayesian model designed after the analysis of the results obtained when evaluating the approach in the context of an Artificial Intelligence course. We evaluated the precision of our Bayesian approach to infer students' learning styles from the observation of their actions with a Web-based education system during three semesters. We show how the results from one semester enabled us to adjust our initial model and helped teachers improve the content of the course for the following semester, enhancing in this way students' learning process. We obtained higher precision values when inferring the learning styles with the enhanced model. [source] Resolving Paternity Relationships Using X-Chromosome STRs and Bayesian NetworksJOURNAL OF FORENSIC SCIENCES, Issue 4 2007Didier Hatsch Ph.D. Abstract:, X-chromosomal short tandem repeats (X-STRs) are very useful in complex paternity cases because they are inherited by male and female offspring in different ways. They complement autosomal STRs (as-STRs) allowing higher paternity probabilities to be attained. These probabilities are expressed in a likelihood ratio (LR). The formulae needed to calculate LR depend on the genotype combinations of suspected pedigrees. LR can also be obtained by the use of Bayesian networks (BNs). These are graphical representations of real situations that can be used to easily calculate complex probabilities. In the present work, two BNs are presented, which are designed to derive LRs for half-sisters/half-sisters and mother/daughter/paternal grandmother relationships. These networks were validated against known formulae and show themselves to be useful in other suspect pedigree situations than those for which they were developed. The BNs were applied in two paternity cases. The application of the mother/daughter/paternal grandmother BN highlighted the complementary value of X-STRs to as-STRs. The same case evaluated without the mother underlined that missing information tends to be conservative if the alleged father is the biological father and otherwise nonconservative. The half-sisters case shows a limitation of statistical interpretations in regard to high allelic frequencies. [source] Join tree propagation with prioritized messagesNETWORKS: AN INTERNATIONAL JOURNAL, Issue 4 2010C. J. Butz Abstract Current join tree propagation algorithms treat all propagated messages as being of equal importance. On the contrary, it is often the case in real-world Bayesian networks that only some of the messages propagated from one join tree node to another are relevant to subsequent message construction at the receiving node. In this article, we propose the first join tree propagation algorithm that identifies and constructs the relevant messages first. Our approach assigns lower priority to the irrelevant messages as they only need to be constructed so that posterior probabilities can be computed when propagation terminates. Experimental results, involving the processing of evidence in four real-world Bayesian networks, empirically demonstrate an improvement over the state-of-the-art method for exact inference in discrete Bayesian networks. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010 [source] Bayesian methods for proteomicsPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 16 2007Gil Alterovitz Dr. Abstract Biological and medical data have been growing exponentially over the past several years [1, 2]. In particular, proteomics has seen automation dramatically change the rate at which data are generated [3]. Analysis that systemically incorporates prior information is becoming essential to making inferences about the myriad, complex data [4,6]. A Bayesian approach can help capture such information and incorporate it seamlessly through a rigorous, probabilistic framework. This paper starts with a review of the background mathematics behind the Bayesian methodology: from parameter estimation to Bayesian networks. The article then goes on to discuss how emerging Bayesian approaches have already been successfully applied to research across proteomics, a field for which Bayesian methods are particularly well suited [7,9]. After reviewing the literature on the subject of Bayesian methods in biological contexts, the article discusses some of the recent applications in proteomics and emerging directions in the field. [source] | |||||||||||||||||||||||||