Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 165 for “"Bayesian Network"”.
-
Bayesian Network Inference Using Marginal Trees
Bayesian networks (BNs) are formal probabilistic graphical models for reasoning un- der uncertainty. BNs are used in a variety of applications, including the state-of- the-art forensic software tool, a ranking system for games, and landing the Mars Exploration Rover. A problem domain is modeled …
-
Bayesian network models of biological signaling pathways
… pathway, from high-throughput data. We apply Bayesian network structure inference to signaling protein measurements performed in thousands of single cells, using a machine called a flow cytorneter. Our de novo reconstruction of a T-cell signaling map was highly accurate, closely reproducing …
-
Bayesian Network Modeling and Inference of GWAS Catalog
… understand genotype-phenotype relationships. The Bayesian network has been proposed as a powerful tool for modeling single-nucleotide polymorphism (SNP)-trait associations due to its advantage in addressing the high computational complex and high dimensional problems. Most current works learn the …
-
A Bayesian Network Model of Political Belief Polarisation
… this process might occur is a prediction of a Bayesian Network model of how people can infer and account for source bias when applied to a simplified political information environment. I begin by establishing the need to explain US mass belief polarisation, and suggest that the dominant …
-
UNCERTAINTY QUANTIFICATION OF LANDSLIDE SUSCEPTIBILITY MAPPING USING BAYESIAN NETWORK
… sample scenarios on LSM development using a Bayesian network model, and 3) a comparative analysis between static and dynamic model structures incorporating a physical slope stability model within a probabilistic machine learning framework. These analyses explore uncertainty derived from …
-
Development of New Cost-Sensitive Bayesian Network Learning Algorithms
Bayesian networks are becoming an increasingly important area for research and have been proposed for real world applications such as medical diagnoses, image recognition, and fraud detection. In all of these applications, accuracy is not sufficient alone, as there are costs involved when errors …
-
Problem dependent metaheuristic performance in Bayesian network structure learning.
Bayesian network (BN) structure learning from data has been an active research area in the machine learning field in recent decades. Much of the research has considered BN structure learning as an optimization problem. However, the finding of optimal BN from data is NP-hard. This fact has driven …
-
Banking regulation: a Bayesian network approach to risk management
… basis against which a novel and comprehensive Bayesian network (BN) methodology for producing VaR and ES forecasts, and those of their stressed counterparts, is assessed in the context of banking regulations, using four learning algorithms. The forecasts generated by the BNs are not found to …
-
Banking regulation: a bayesian network approach to risk management
… basis against which a novel and comprehensive Bayesian network (BN) methodology for producing VaR and ES forecasts, and those of their stressed counterparts, is assessed in the context of banking regulations, using four learning algorithms. The forecasts generated by the BNs are not found to …
-
Efficient Bayesian Network Inference: Genetic Algorithms, Stochastic Local Search, and Abstraction
… presented that relate to creating hard synthetic Bayesian networks for empirical research on inference algorithms. One method translates deceptive problems studied in genetic algorithms to a Bayesian network setting, showing that Bayesian networks can be deceptive. The other result is based on …
-
A semantic Bayesian network for automated share evaluation on the JSE
… the efficacy of using ontologies and Bayesian networks for automating share evaluation on the JSE. The knowledge acquired from an analysis of the investment domain and the decision-making process for a value investing approach was represented in an ontology. A Bayesian network was …
-
Multinet Bayesian network models for large-scale transcriptome integration in computational medicine
Motivation: This work utilizes the closed loop Bayesian network framework for predictive medicine via integrative analysis of publicly available gene expression findings pertaining to various diseases and analyzes the results to determine which model, single net or multinet, is a more accurate …
-
Development of a Bayesian Network to monitor the probability of nuclear proliferation
… with preventing further nuclear proliferation. Bayesian Inference is a tool of quantitative analysis that is rapidly gaining interest in numerous fields of scientific study that have previously been limited to purely statistical methods. The Bayesian approach removes the statistical limitations …
-
An enhanced Bayesian Network prediction model for football matches based on player performance
… existing researches have showed that the Bayesian networks (BN) approach has greatly contributed to predicting football match results with considerably high accuracy as compared to other classical statistical and machine learning approaches. However, existing prediction models rely solely …
-
A Bayesian network framework for fusing spectrum-based fault localization and forward slicing
… SBFL metrics with forward slicing through Bayesian networks. Unlike prior hybrids that combine these techniques via fixed heuristics, this approach models the probabilistic relationships between suspiciousness scores and slicing reachability, enabling principled evidence integration. This …
-
A Bayesian Network Approach to the Self-organization and Learning in Intelligent Agents
A Bayesian network approach to self-organization and learning is introduced for use with intelligent agents. Bayesian networks, with the help of influence diagrams, are employed to create a decision-theoretic intelligent agent. Influence diagrams combine both Bayesian networks and utility theory. …
Page 1 of 9