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Articles 1 - 12 of 12
Full-Text Articles in Physical Sciences and Mathematics
Using Bayesian Networks To Provide Educational Implications: Mobile Learning And Ethnomathematics To Improve Sustainability In Mathematics Education, Jason D. Johnson, Linda Smail, Darryl Corey, Adeeb M. Jarrah
Using Bayesian Networks To Provide Educational Implications: Mobile Learning And Ethnomathematics To Improve Sustainability In Mathematics Education, Jason D. Johnson, Linda Smail, Darryl Corey, Adeeb M. Jarrah
All Works
There are many Western apps that help students strengthen their mathematics skills through learning and game apps. A research project was designed to create an IOS Math App to provide Grade 6 Emirati students with the opportunity to explore mathematics, then, using Bayesian Networks, to examine the educational implications. The learning app was developed using ethnomathematics modules based on the Emirati culture. Students were required to navigate through several modules to examine various mathematical concepts in algebra and geometry. The survey was written for Grade 6 English language learners. Based on the Bayesian Networks, the findings suggested that if students …
Multilevel Hierarchical Decomposition Of Finite Element White Noise With Application To Multilevel Markov Chain Monte Carlo, Hillary R. Fairbanks, Umberto E. Villa, Panayot S. Vassilevski
Multilevel Hierarchical Decomposition Of Finite Element White Noise With Application To Multilevel Markov Chain Monte Carlo, Hillary R. Fairbanks, Umberto E. Villa, Panayot S. Vassilevski
Mathematics and Statistics Faculty Publications and Presentations
In this work we develop a new hierarchical multilevel approach to generate Gaussian random field realizations in an algorithmically scalable manner that is well suited to incorporating into multilevel Markov chain Monte Carlo (MCMC) algorithms. This approach builds off of other partial differential equation (PDE) approaches for generating Gaussian random field realizations; in particular, a single field realization may be formed by solving a reaction-diffusion PDE with a spatial white noise source function as the right-hand side. While these approaches have been explored to accelerate forward uncertainty quantification tasks, e.g., multilevel Monte Carlo, the previous constructions are not directly applicable …
Estimating Posterior Quantity Of Interest Expectations In A Multilevel Scalable Framework, Hillary R. Fairbanks, Sarah Osborn, Panayot S. Vassilevski
Estimating Posterior Quantity Of Interest Expectations In A Multilevel Scalable Framework, Hillary R. Fairbanks, Sarah Osborn, Panayot S. Vassilevski
Mathematics and Statistics Faculty Publications and Presentations
Scalable approaches for uncertainty quantification are necessary for characterizing prediction confidence in large‐scale subsurface flow simulations with uncertain permeability. To this end we explore a multilevel Monte Carlo approach for estimating posterior moments of a particular quantity of interest, where we employ an element‐agglomerated algebraic multigrid (AMG) technique to generate the hierarchy of coarse spaces with guaranteed approximation properties for both the generation of spatially correlated random fields and the forward simulation of Darcy's law to model subsurface flow. In both these components (sampling and forward solves), we exploit solvers that rely on state‐of‐the‐art scalable AMG. To showcase the applicability …
Junction Trees Constructions In Bayesian Networks, Linda Smail
Junction Trees Constructions In Bayesian Networks, Linda Smail
All Works
© Published under licence by IOP Publishing Ltd. Junction trees are used as graphical structures over which propagation will be carried out through a very important property called the ruining intersection property. This paper examines an alternative method for constructing junction trees that are essential for the efficient computations of probabilities in Bayesian networks. The new proposed method converts a sequence of subsets of a Bayesian network into a junction tree, in other words, into a set of cliques that has the running intersection property. The obtained set of cliques and separators coincide with the junction trees obtained by the …
Using Bayesian Networks To Understand Relationships Among Math Anxiety, Genders, Personality Types, And Study Habits At A University In Jordan, Linda Smail
All Works
© 2017 Journal on Mathematics Education.All Rights Reserved. Mathematics is the foundation of all sciences, but most students have problems learning math. Although students' success in life related to their success in learning, many would not take a math course unless it is their university's core requirements. Multiple reasons exist for students' poor performance in mathematics, but one prevalent variable worth consideration is the personality type. This work seeks to uncover relationships, if any, between students' math anxiety and the students' learning type in learning math and preparing for exams and tests. We use Bayesian networks to link those different …
Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar
Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar
Research Collection School Of Computing and Information Systems
We present a new perspective on the classical shortest path routing (SPR) problem in graphs. We show that the SPR problem can be recast to that of probabilistic inference in a mixture of simple Bayesian networks. Maximizing the likelihood in this mixture becomes equivalent to solving the SPR problem. We develop the well known Expectation-Maximization (EM) algorithm for the SPR problem that maximizes the likelihood, and show that it does not get stuck in a locally optimal solution. Using the same probabilistic framework, we then address an NP-Hard network design problem where the goal is to repair a network of …
Development Of A Stakeholder-Driven Spatial Modeling Framework For Strategic Landscape Planning Using Bayesian Networks Across Two Urban-Rural Gradients In Maine, Usa, Spencer Meyer, Michelle Johnson, Robert Lilieholm, Christopher Cronan
Development Of A Stakeholder-Driven Spatial Modeling Framework For Strategic Landscape Planning Using Bayesian Networks Across Two Urban-Rural Gradients In Maine, Usa, Spencer Meyer, Michelle Johnson, Robert Lilieholm, Christopher Cronan
Publications
Land use change results from frequent, independent actions by decision-makers working in isolation, often with a focus on a single land use. In order to develop integrated land use policies that encourage sustainable outcomes, scientists and practitioners must understand the specific drivers of land use change across mixed land use types and ownerships, and must consider the combined influences of biophysical, economic, and social factors that affect land use decisions. In this analysis of two large watersheds covering a total of 1.9 million hectares in Maine, USA, we co-developed with groups of stakeholders land use suitability models that integrated four …
Beyond Home Automation: Designing More Effective Smart Home Systems, Paolo Carner
Beyond Home Automation: Designing More Effective Smart Home Systems, Paolo Carner
9th. IT & T Conference
This paper outlines a Smart Home Proof-of-Concept system that uses a Bayesian Network to predict the likelihood of a monitored event to occur. Firstly, this paper will provide an introduction to the concept of a smart home system; then it will outline how Artificial Intelligence concepts can be used to make such systems more effective. Finally, it will detail the implementation of a smart home system, which uses an inference engine to determine the likelihood of a fire. The system prototype has implemented using a LonWorks™ hardware kit and a Netica™ Bayesian Network engine from Norsys.
Active Learning For Causal Bayesian Network Structure With Non-Symmetrical Entropy, Li G., Tze-Yun Leong
Active Learning For Causal Bayesian Network Structure With Non-Symmetrical Entropy, Li G., Tze-Yun Leong
Research Collection School Of Computing and Information Systems
Causal knowledge is crucial for facilitating comprehension, diagnosis, prediction, and control in automated reasoning. Active learning in causal Bayesian networks involves interventions by manipulating specific variables, and observing the patterns of change over other variables to derive causal knowledge. In this paper, we propose a new active learning approach that supports interventions with node selection. Our method admits a node selection criterion based on non-symmetrical entropy from the current data and a stop criterion based on structure entropy of the resulting networks. We examine the technical challenges and practical issues involved. Experimental results on a set of benchmark Bayesian networks …
Explaining Inferences In Bayesian Networks, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Explaining Inferences In Bayesian Networks, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
While Bayesian network (BN) can achieve accurate predictions even with erroneous or incomplete evidence, explaining the inferences remains a challenge. Existing approaches fall short because they do not exploit variable interactions and cannot account for compensations during inferences. This paper proposes the Explaining BN Inferences (EBI) procedure for explaining how variables interact to reach conclusions. EBI explains the value of a target node in terms of the influential nodes in the target's Markov blanket under specific contexts, where the Markov nodes include the target's parents, children, and the children's other parents. Working back from the target node, EBI shows the …
Predicting Coronary Artery Disease With Medical Profile And Gene Polymorphisms Data, Qiongyu Chen, Guoliang Li, Tze-Yun Leong, Chew-Kiat Heng
Predicting Coronary Artery Disease With Medical Profile And Gene Polymorphisms Data, Qiongyu Chen, Guoliang Li, Tze-Yun Leong, Chew-Kiat Heng
Research Collection School Of Computing and Information Systems
Coronary artery disease (CAD) is a main cause of death in the world. Finding cost-effective methods to predict CAD is a major challenge in public health. In this paper, we investigate the combined effects of genetic polymorphisms and non-genetic factors on predicting the risk of CAD by applying well known classification methods, such as Bayesian networks, naïve Bayes, support vector machine, k-nearest neighbor, neural networks and decision trees. Our experiments show that all these classifiers are comparable in terms of accuracy, while Bayesian networks have the additional advantage of being able to provide insights into the relationships among the variables. …
Discovering And Exploiting Causal Dependencies For Robust Mobile Context-Aware Recommenders, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Discovering And Exploiting Causal Dependencies For Robust Mobile Context-Aware Recommenders, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
Acquisition of context poses unique challenges to mobile context-aware recommender systems. The limited resources in these systems make minimizing their context acquisition a practical need, and the uncertainty in the mobile environment makes missing and erroneous context inputs a major concern. In this paper, we propose an approach based on Bayesian networks (BNs) for building recommender systems that minimize context acquisition. Our learning approach iteratively trims the BN-based context model until it contains only the minimal set of context parameters that are important to a user. In addition, we show that a two-tiered context model can effectively capture the causal …