Unifying Gaussian LWF and AMP Chain Graphs to Model Interference

An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian distributions. Specifically, we define the new class of models, introduce global and local and pairwise Markov properties for them, and prove their equivalence. We also propose an algorithm for maximum likelihood parameter estimation for the new models, and report experimental results. Finally, we show how to compute the effects of interventions in the new models.Comment: v2: Section 6 has been added. v3: Sections 7 and 8 have been added. v4: Major reorganization. v5: Major reorganization. v6-v7: Minor changes. v8: Addition of Appendix B. v9: Section 7 has been rewritte

Properties, Learning Algorithms, and Applications of Chain Graphs and Bayesian Hypergraphs

Probabilistic graphical models (PGMs) use graphs, either undirected, directed, or mixed, to represent possible dependencies among the variables of a multivariate probability distri- bution. PGMs, such as Bayesian networks and Markov networks, are now widely accepted as a powerful and mature framework for reasoning and decision making under uncertainty in knowledge-based systems. With the increase of their popularity, the range of graphical models being investigated and used has also expanded. Several types of graphs with dif- ferent conditional independence interpretations - also known as Markov properties - have been proposed and used in graphical models. The graphical structure of a Bayesian network has the form of a directed acyclic graph (DAG), which has the advantage of supporting an interpretation of the graph in terms of cause-effect relationships. However, a limitation is that only asymmetric relationships, such as cause and effect relationships, can be modeled between variables in a DAG. Chain graphs, which admit both directed and undirected edges, can be used to overcome this limitation. Today there exist three main different interpretations of chain graphs in the lit- erature. These are the Lauritzen-Wermuth-Frydenberg, the Andersson-Madigan-Perlman, and the multivariate regression interpretations. In this thesis, we study these interpreta- tions based on their separation criteria and the intuition behind their edges. Since structure learning is a critical component in constructing an intelligent system based on a chain graph model, we propose new feasible and efficient structure learning algorithms to learn chain graphs from data under the faithfulness assumption. The proliferation of different PGMs that allow factorizations of different kinds leads us to consider a more general graphical structure in this thesis, namely directed acyclic hypergraphs. Directed acyclic hypergraphs are the graphical structure of a new proba- bilistic graphical model that we call Bayesian hypergraphs. Since there are many more hypergraphs than DAGs, undirected graphs, chain graphs, and, indeed, other graph-based networks, Bayesian hypergraphs can model much finer factorizations and thus are more computationally efficient. Bayesian hypergraphs also allow a modeler to represent causal patterns of interaction such as Noisy-OR graphically (without additional annotations). We introduce global, local and pairwise Markov properties of Bayesian hypergraphs and prove under which conditions they are equivalent. We also extend the causal interpretation of LWF chain graphs to Bayesian hypergraphs and provide corresponding formulas and a graphical criterion for intervention. The framework of graphical models, which provides algorithms for discovering and analyzing structure in complex distributions to describe them succinctly and extract un- structured information, allows them to be constructed and utilized effectively. Two of the most important applications of graphical models are causal inference and information ex- traction. To address these abilities of graphical models, we conduct a causal analysis, comparing the performance behavior of highly-configurable systems across environmen- tal conditions (changing workload, hardware, and software versions), to explore when and how causal knowledge can be commonly exploited for performance analysis

Para além de pressupostos psicométricos : como desenvolver novas medidas psicológicas

Tese (doutorado)—Universidade de Brasília, Instituto de Psicologia, Programa de Pós-graduação em Psicologia Social, do Trabalho e das Organizações, 2019.O que define uma boa medida? Na presente tese, argumentamos e mostramos que definir uma boa medida pode ser muito mais complexo do que simplesmente executar uma análise fatorial ou uma análise usando a teoria da resposta ao item. O objetivo geral desta dissertação é apresentar três principais pressupostos da medida psicométrica e desenvolver alternativas para a medida psicológica tradicional. A tese está dividida em quatro estudos. O primeiro é um estudo teórico no qual são apresentados três pressupostos centrais comuns à teoria psicométrica e à prática psicométrica, e no qual é mostrado como alternativas às abordagens psicométricas tradicionais podem ser usadas para melhorar a medição psicológica. Essas alternativas foram desenvolvidas adaptando cada um desses três pressupostos: (1) o pressuposto de validade estrutural; (2) o pressuposto do processo; e (3) o pressuposto de construto. O pressuposto de validade estrutural refere-se à implementação de modelos matemáticos. O pressuposto de processo implica que um processo subjacente específico está gerando os dados observados. O pressuposto de construto infere que os dados observados por si só não constituem uma medida, mas que as medidas são as variáveis latentes que originam os dados observados. Vários exemplos de abordagens psicométricas alternativas já existentes são apresentados no primeiro estudo. O segundo estudo se refere ao pressuposto de validade estrutural e teve como objetivo desenvolver dois novos modelos de resposta aos itens para itens politômicos e binários que não assumem uma distribuição normal dos escores verdadeiros. O primeiro modelo desenvolvido, o Modelo de resposta ao item condicional (CIRM), assume uma distribuição beta- binomial. O segundo novo modelo é uma implementação Bayesiana do procedimento de escore ótimo (OS-IRM). Ambos os novos modelos foram comparados com o modelo tradicional de Rasch: os resultados indicam que os dois modelos desenvolvidos melhoram vários aspectos do modelo de Rasch. O terceiro estudo foi derivado do pressuposto do processo e tinha três objetivos. Primeiro, desenvolver uma implementação Bayesiana do framework de análise da função de otimização situacional (SOFA). Segundo, comparar essa implementação Bayesiana do SOFA com outros três modelos baseados em Máxima Verossimilhança, usados para estimar escores verdadeiros. O terceiro objetivo foi mostrar como a modelagem conjunta pode ser usada para pesquisas de validade. Uma das principais vantagens do framework SOFA em comparação com a abordagem psicométrica tradicional é que o SOFA depende de dados experimentais, melhorando a validade das medidas. O quarto e último estudo foi derivado do pressuposto de construto e seu principal objetivo era desenvolver um procedimento de aprendizado de estrutura de gráficos de cadeia de potência (PCGs). Um PCG é um tipo de gráfico que representa relações causais entre grupos de variáveis. Pode ser pensado como uma versão exploratória completa da modelagem de equações estruturais, bem como um modelo psicométrico que não depende de variáveis latentes. Esses quatro estudos pretendem mostrar que a modelagem psicométrica não deve se restringir ao uso de modelos tradicionais de mensuração, mas também deve considerar a adaptação desses modelos tradicionais de acordo com o uso pretendido e os processos teóricos que originam as medidas observadas.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).What defines a good measurement? In the present dissertation we argue, and show, that defining a good measurement can be much more complex than simply performing a factor analysis or an analysis using item response theory. The overall objective of this dissertation is to present three principal assumptions of psychometric measurement, and to develop alternatives for traditional psychological measurement. The dissertation is divided in four studies. The first one is a theoretical study in which three central assumptions common to psychometric theory and psychometric practice are presented, and in which is shown how alternatives to traditional psychometric approaches can be used to improve psychological measurement. These alternatives were developed by adapting each of these three assumptions: (1) the assumption of structural validity; (2) the process assumption; and, (3) the construct assumption. The structural validity assumption relates to the implementation of mathematical models. The process assumption implies that a specific underlying process is generating the observed data. The construct assumption infers that the observed data on its own do not constitute a measurement, but the measure are the latent variables that originate the observed data. Several examples of already existing alternative psychometric approaches are presented in the first study. The second study relates to the structural validity assumption and aimed to develop two new item response models for polytomous and binary items that do not assume a normal distribution of the true scores. The first model that was developed, the Conditional Item Response Model (CIRM), assumes a beta-binomial distribution. The second new model is a Bayesian implementation of the optimal score procedure (OS-IRM). Both new models were compared with the traditional Rasch model: the results indicate that the two developed models improve various aspects of the Rasch model. The third study was derived from the process assumption and had three objectives. First, to develop a Bayesian implementation of the situational optimization function analysis (SOFA) framework. Second, to compare this Bayesian implementation of SOFA with three other Maximum Likelihood-based models that are used to estimate true scores. The third objective was to show how joint modeling can be used for validity research. One of the main advantages of the SOFA framework compared to the traditional psychometric approach is that SOFA relies on experimental data, improving the validity of the measures. The fourth and final study was derived from the construct assumption and its main objective was to develop a procedure of structure learning of power chain graphs (PCGs). A PCG is a type of graph that represents causal relations between groups of variables. It can be thought as a full exploratory version of structural equation modeling, as well as a psychometric model that is not dependent on latent variables. These four studies intend to show that psychometric modeling should not be restricted to the use of traditional measurement models, but should also consider adapting these traditional models in accordance with the intended use and theoretical processes that originate the observed measures

Unifying Gaussian LWF and AMP Chain Graphs to Model Interference

An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian distributions. Specifically, we define the new class of models, introduce global and local and pairwise Markov properties for them, and prove their equivalence. We also propose an algorithm for maximum likelihood parameter estimation for the new models, and report experimental results. Finally, we show how to compute the effects of interventions in the new models

Collected Papers (Neutrosophics and other topics), Volume XIV

This fourteenth volume of Collected Papers is an eclectic tome of 87 papers in Neutrosophics and other fields, such as mathematics, fuzzy sets, intuitionistic fuzzy sets, picture fuzzy sets, information fusion, robotics, statistics, or extenics, comprising 936 pages, published between 2008-2022 in different scientific journals or currently in press, by the author alone or in collaboration with the following 99 co-authors (alphabetically ordered) from 26 countries: Ahmed B. Al-Nafee, Adesina Abdul Akeem Agboola, Akbar Rezaei, Shariful Alam, Marina Alonso, Fran Andujar, Toshinori Asai, Assia Bakali, Azmat Hussain, Daniela Baran, Bijan Davvaz, Bilal Hadjadji, Carlos Díaz Bohorquez, Robert N. Boyd, M. Caldas, Cenap Özel, Pankaj Chauhan, Victor Christianto, Salvador Coll, Shyamal Dalapati, Irfan Deli, Balasubramanian Elavarasan, Fahad Alsharari, Yonfei Feng, Daniela Gîfu, Rafael Rojas Gualdrón, Haipeng Wang, Hemant Kumar Gianey, Noel Batista Hernández, Abdel-Nasser Hussein, Ibrahim M. Hezam, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Muthusamy Karthika, Nour Eldeen M. Khalifa, Madad Khan, Kifayat Ullah, Valeri Kroumov, Tapan Kumar Roy, Deepesh Kunwar, Le Thi Nhung, Pedro López, Mai Mohamed, Manh Van Vu, Miguel A. Quiroz-Martínez, Marcel Migdalovici, Kritika Mishra, Mohamed Abdel-Basset, Mohamed Talea, Mohammad Hamidi, Mohammed Alshumrani, Mohamed Loey, Muhammad Akram, Muhammad Shabir, Mumtaz Ali, Nassim Abbas, Munazza Naz, Ngan Thi Roan, Nguyen Xuan Thao, Rishwanth Mani Parimala, Ion Pătrașcu, Surapati Pramanik, Quek Shio Gai, Qiang Guo, Rajab Ali Borzooei, Nimitha Rajesh, Jesús Estupiñan Ricardo, Juan Miguel Martínez Rubio, Saeed Mirvakili, Arsham Borumand Saeid, Saeid Jafari, Said Broumi, Ahmed A. Salama, Nirmala Sawan, Gheorghe Săvoiu, Ganeshsree Selvachandran, Seok-Zun Song, Shahzaib Ashraf, Jayant Singh, Rajesh Singh, Son Hoang Le, Tahir Mahmood, Kenta Takaya, Mirela Teodorescu, Ramalingam Udhayakumar, Maikel Y. Leyva Vázquez, V. Venkateswara Rao, Luige Vlădăreanu, Victor Vlădăreanu, Gabriela Vlădeanu, Michael Voskoglou, Yaser Saber, Yong Deng, You He, Youcef Chibani, Young Bae Jun, Wadei F. Al-Omeri, Hongbo Wang, Zayen Azzouz Omar

Towards large eddy simulation of dispersed gas -liquid two-phase turbulent flows

This study presents a detailed investigation of all essential components of computational and modeling issues necessary for a successful large-eddy simulation (LES) of dispersed two-phase turbulent flows. In particular, a two-layer concept is proposed to enable the LES capability in two-phase flows involving dispersed bubbles that are relatively large compared to the mesh size. The work comprises three major parts.;Part I focuses on the development and verification of a transient, three-dimensional, finite-volume-method (FVM) based accurate Navier-Stokes solver, named DREAM II (second generation of the DREAM code). Several high-order schemes are implemented for both the spatial and temporal discretization. Solution of the coupled partial differential equations is attacked with a fractional step (projection) method. The developed solver is verified against various benchmarks including Taylor\u27s vortex, free-shear layer, backward-facing step flow and square cavity. A second-order overall accuracy is achieved in both space and time.;Part II concerns the modeling and LES of single-phase turbulent flows. A review of the LES theory and subgrid-scale (SGS) models is presented. Three SGS models, namely, Smagorinsky model, dynamic model and implicit model, are implemented and investigated. Then turbulent channel flow, plane mixing layer, and flow past a square cylinder are simulated, and comparisons of the first-, second-order statistics, and characteristic flow structures are made with direct numerical simulation (DNS) and/or benchmark experiments. The test results show superior quality of the present LES.;Part III delves into the theory, modeling and simulation of dispersed two-phase flow systems. A conceptual review of the characteristics and description of such system is made, considering both Eulerian-Eulerian (E-E) and Eulerian-Lagrangian (E-L) approaches, but with an emphasis on the latter. Various hydrodynamic forces acting on particles or bubbles are summarized and interpreted. Formulations regarding interphase coupling is discussed in depth. Typical computational treatments of modeled two-way couplings in an E-L DNS/LES are reviewed. Issues related to the interpolation are addressed. A general Lagrangian particle-tracking (LPT) program, named PART, is developed and verified using analytical solutions. (Abstract shortened by UMI.)