Even though simulation technology provides great benefits to industry, it is largely underutilized. One of the biggest barriers to utilizing simulation is the lack of interoperability between simulation models. This is especially true when simulation models that need to interact with each other span an enterprise or supply chain. These models are likely to be distributed and developed in disparate simulation application software. In order to analyze the dynamic behavior of the systems they represent, the models must interoperate. However, currently this interoperability is nearly impossible. The interaction of models also refers to the understanding of them among stakeholders in the different stages of models¡Š lifecycles. The lack of interoperability also makes it difficult to share the knowledge within disparate models. This research first investigates this problem by identifying, defining, and analyzing the types of simulation model interactions. It then identifies and defines possible approaches to allow models to interact. Finally, a framework that adopts the strength of Structured Modeling (SM) and the Object-Oriented (OO) concept is proposed for representing discrete event simulation models. The framework captures the most common simulation elements and will serve as an intermediate language between disparate simulation models. Because of the structured nature of the framework, the resulting model representation is concise and easily understandable. Tools are developed to implement the framework. A Common User Interface (CUI) with software specified controllers is developed for using the proposed framework with various commercial simulation software packages. The CUI is also used to edit simulation models in a neutral environment. A graphical modeling tool is also developed to facilitate conceptual modeling. The resulting graphic can be translated into the common model representation automatically. This not only increases the understanding of models for all stakeholders, but also shifts model interactions to the ¡§formulating¡š stage, which can prevent problems later in the model¡Šs lifecycle. Illustration of the proposed framework and the tools will be given, as well as future work needs.

<p dir="ltr">In recent years, probabilistic graphical models have emerged as a powerful framework for understanding complex dependencies in multivariate data, offering a structured approach to tackle uncertainty and model complexity. These models have revolutionized the way we interpret the interplay between variables in various domains, from genetics to social network analysis. Inspired by the potential of probabilistic graphical models to provide insightful data analysis while addressing the challenges of high-dimensionality and computational efficiency, this dissertation introduces two novel methodologies that leverage the strengths of graphical models in high-dimensional settings. By integrating advanced inference techniques and exploiting the structural advantages of graphical models, we demonstrate how these approaches can efficiently decode complex data patterns, offering significant improvements over traditional methods. This work not only contributes to the theoretical advancements in the field of statistical data analysis but also provides practical solutions to real-world problems characterized by large-scale, complex datasets.</p><p dir="ltr">Firstly, we introduce a novel Bayesian hybrid method for learning the structure of Gaus- sian Bayesian Networks (GBNs), addressing the critical challenge of order determination in constraint-based and score-based methodologies. By integrating a permutation matrix within the likelihood function, we propose a technique that remains invariant to data shuffling, thereby overcoming the limitations of traditional approaches. Utilizing Cholesky decompo- sition, we reparameterize the log-likelihood function to facilitate the identification of the parent-child relationship among nodes without relying on the faithfulness assumption. This method efficiently manages the permutation matrix to optimize for the sparsest Cholesky factor, leveraging the Bayesian Information Criterion (BIC) for model selection. Theoretical analysis and extensive simulations demonstrate the superiority of our method in terms of precision, recall, and F1-score across various network complexities and sample sizes. Specifically, our approach shows significant advantages in small-n-large-p scenarios, outperforming existing methods in detecting complex network structures with limited data. Real-world applications on datasets such as ECOLI70, ARTH150, MAGIC-IRRI, and MAGIC-NIAB further validate the effectiveness and robustness of our proposed method. Our findings contribute to the field of Bayesian network structure learning by providing a scalable, efficient, and reliable tool for modeling high-dimensional data structures.</p><p dir="ltr">Secondly, we introduce a Bayesian methodology tailored for Gaussian Graphical Models (GGMs) that bridges the gap between GBNs and GGMs. Utilizing the Cholesky decomposition, we establish a novel connection that leverages estimated GBN structures to accurately recover and estimate GGMs. This innovative approach benefits from a theoretical foundation provided by a theorem that connects sparse priors on Cholesky factors with the sparsity of the precision matrix, facilitating effective structure recovery in GGMs. To assess the efficacy of our proposed method, we conduct comprehensive simulations on AR2 and circle graph models, comparing its performance with renowned algorithms such as GLASSO, CLIME, and SPACE across various dimensions. Our evaluation, based on metrics like estimation ac- curacy and selection correctness, unequivocally demonstrates the superiority of our approach in accurately identifying the intrinsic graph structure. The empirical results underscore the robustness and scalability of our method, underscoring its potential as an indispensable tool for statistical data analysis, especially in the context of complex datasets.</p>

Based on these observations and analysis, we propose a joint discriminative probabilistic framework to optimize all relevant subtasks simultaneously. This framework defines a joint probability distribution for both segmentations in sequence data and relations of segments in the form of an exponential family. This model allows tight interactions between segmentations and relations of segments and it offers a natural way for IE tasks. Since exact parameter estimation and inference are prohibitively intractable, a structured variational inference algorithm is developed to perform parameter estimation approximately. For inference, we propose a strong bi-directional MH approach to find the MAP assignments for joint segmentations and relations to explore mutual benefits on both directions, such that segmentations can aid relations, and vice-versa. / Information Extraction (IE) aims at identifying specific pieces of information (data) in a unstructured or semi-structured textual document and transforming unstructured information in a corpus of documents or Web pages into a structured database. There are several representative tasks in IE: named entity recognition (NER), which aims at identifying phrases that denote types of named entities, entity relation extraction, which aims at discovering the events or relations related to the entities, and the task of coreference resolution, aims at determining whether two extracted mentions of entities refer to the same object. IE is useful for a wide variety of applications. / The end-to-end performance of high-level IE systems for compound tasks is often hampered by the use of cascaded frameworks. The integrated model we proposed can alleviate some of these problems, but it is only loosely coupled. Parameter estimation is performed independently and it only allows information to flow in one direction. In this top-down integration model, the decision of the bottom sub-model could guide the decision of the upper sub-model, but not vice-versa. Thus, deep interactions and dependencies between different tasks can hardly be well captured. / We have investigated and developed a cascaded framework in an attempt to consider entity extraction and qualitative domain knowledge based on undirected, discriminatively-trained probabilistic graphical models. This framework consists of two stages and it is the combination of statistical learning and first-order logic. As a pipeline model, the first stage is a base model and the second stage is used to validate and correct the errors made in the base model. We incorporated domain knowledge that can be well formulated into first-order logic to extract entity candidates from the base model. We have applied this framework and achieved encouraging results in Chinese NER on the People's Daily corpus. / We perform extensive experiments on three important IE tasks using real-world datasets, namely Chinese NER, entity identification and relationship extraction from Wikipedia's encyclopedic articles, and citation matching, to test our proposed models, including the bidirectional model, the integrated model, and the joint model. Experimental results show that our models significantly outperform current state-of-the-art probabilistic models, such as decoupled and joint models, illustrating the feasibility and promise of our proposed approaches. (Abstract shortened by UMI.) / We present a general, strongly-coupled, and bidirectional architecture based on discriminatively trained factor graphs for information extraction, which consists of two components---segmentation and relation. First we introduce joint factors connecting variables of relevant subtasks to capture dependencies and interactions between them. We then propose a strong bidirectional Markov chain Monte Carlo (MCMC) sampling inference algorithm which allows information to flow in both directions to find the approximate maximum a posteriori (MAP) solution for all subtasks. Notably, our framework is considerably simpler to implement, and outperforms previous ones. / Yu, Xiaofeng. / Adviser: Zam Wai. / Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 109-123). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Domain Specific Modeling (DSM) aims to increase productivity of software development by raising the level of abstraction beyond code concepts and using domain concepts. By providing a generative model-driven tooling component and runtime support, the Eclipse Graphical Modeling Framework (GMF) aims to simplify the creation of diagram editors for specific domains based on a series of model creation and transformation steps. GMF leverages the Eclipse Modeling Framework (EMF) and the Eclipse Graphical Editing Framework (GEF) to allow the graphical modeling of Domain Specific Languages (DSL). A Domain Specific Language (DSL) is developed specifically for a specific task and specific domain. In this research, the State Machine Compiler (SMC) represents the specific domain for which a DSL in a form of a diagram editor is developed using GMF. SMC is an open source Java tool allowing generation of state pattern classes from textual descriptions of state machines. The main objective of this research is to describe the use of GMF, highlight potential pitfalls and identify strengths and weaknesses of GMF based on certain criteria. To be able to feed the SMC diagrams created with the editor into SMC, a Java Emitter Templates (JET) transformation is used to transform SMC model instances into textual format expected by SMC. / Thesis (Master, Computing) -- Queen's University, 2011-04-14 18:58:08.797

A Cyber-Physical System (CPS) has a tight integration of computation, networking and physicalprocess. It is a heterogeneous system that combines multi-domain consisting of both hardware andsoftware systems. Cyber subsystems in the CPS implement the control strategy that affects the physicalprocess. Therefore, software systems in the CPS are more complex. Visualization of a complex system provides a method of understanding complex systems byaccumulating, grouping, and displaying components of systems in such a manner that they may beunderstood more efficiently just by viewing the model rather than understanding the code. Graphicalrepresentation of complex systems provides an intuitive and comprehensive way to understand thesystem. OpenModelica is the open source development environment based on Modelica modeling andsimulation language that consists of several interconnected subsystems. OMEdit is one of the subsystemintegrated into OpenModelica. It is a graphical user interface for graphical modeling. It consists of toolsthat allow the user to create their own shapes and icons for the model. This thesis presents a methodology that provides an easy way of understanding the structure andexecution of programs written in the imperative language like C through graphical Modelica model.

Project (M.S.)--Brigham Young University. Dept. of Statistics, 2007. / Includes bibliographical references (p. 60-61).

Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 140-150).

Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: J.K. Aggarwal. Vita. Includes bibliographical references.

This paper deals with the creation of the tool for chip design in UML. The intention of this work is to present the news in the UML language version 2.0, that can be possibly used for modeling of embedded systems. Furthermore, it deals with the possibility and method of modeling in the Eclipse environment and it focuses on the Eclipse Modeling Framework. This work explains the principle of developing of graphical editors based on GMF used fully by developing tool. Finally, it discusses the chosen solution.

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