Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables

2021 ◽  
Vol 22 (9) ◽  
pp. 1234-1246
Author(s):  
Yuxue Xu ◽  
Yun Wang ◽  
Tianhong Yan ◽  
Yuchen He ◽  
Jun Wang ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Latent Variables ◽  
Stationary Processes ◽  
Soft Sensing

scholarly journals РЕАЛІЗАЦІЯ МЕТОДИКИ ПОБУДОВИ МОДЕЛЕЙ НЕЛІНІЙНИХ НЕСТАЦІОНАРНИХ ПРОЦЕСІВ В ЕКОНОМІЦІ ТА ФІНАНСАХ ТА ЗАСТОСУВАННЯ МЕРЕЖІ БАЙЄСА ДЛЯ ПОКРАЩЕННЯ РІВНЯ АДЕКВАТНОСТІ МОДЕЛЕЙ ТА ЯКОСТІ ОЦІНОК КОРОТКОСТРОКОВИХ ПРОГНОЗІВ

World Science ◽  
2018 ◽  
Vol 1 (8(36)) ◽  
pp. 4-10
Author(s):  
Дудка Б. Р.
Keyword(s):  
Regression Analysis ◽  
Bayesian Network ◽  
System Analysis ◽  
Stationary Processes ◽  
Short Term

In this article was represented implementation of methodology of nonlinear non stationary processes modeling in economy and finances which based on different conceptions of system analysis, regression analysis and applying of bayesian network to improve estimation of short term forecasts.

scholarly journals Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning

2020 ◽  
Author(s):  
Mehdi Momen ◽  
Madhav Bhatta ◽  
Waseem Hussain ◽  
Haipeng Yu ◽  
Gota Morota
Keyword(s):  
Factor Analysis ◽  
Bayesian Network ◽  
Grain Yield ◽  
Leaf Rust ◽  
Exploratory Factor Analysis ◽  
Stem Rust ◽  
Latent Variables ◽  
Yellow Rust ◽  
Flag Leaf ◽  
Data Driven

AbstractInferring trait networks from a large volume of genetically correlated diverse phenotypes such as yield, architecture, and disease resistance can provide information on the manner in which complex phenotypes are interrelated. However, studies on statistical methods tailored to multi-dimensional phenotypes are limited, whereas numerous methods are available for evaluating the massive number of genetic markers. Factor analysis operates at the level of latent variables predicted to generate observed responses. The objectives of this study were to illustrate the manner in which data-driven exploratory factor analysis can map observed phenotypes into a smaller number of latent variables and infer a genomic latent factor network using 45 agro-morphological, disease, and grain mineral phenotypes measured in synthetic hexaploid wheat lines (Triticum Aestivum L.). In total, eight latent factors including grain yield, architecture, flag leaf-related traits, grain minerals, yellow rust, two types of stem rust, and leaf rust were identified as common sources of the observed phenotypes. The genetic component of the factor scores for each latent variable was fed into a Bayesian network to obtain a trait structure reflecting the genetic interdependency among traits. Three directed paths were consistently identified by two Bayesian network algorithms. Flag leaf-related traits influenced leaf rust, and yellow rust and stem rust influenced grain yield. Additional paths that were identified included flag leaf-related traits to minerals and minerals to architecture. This study shows that data-driven exploratory factor analysis can reveal smaller dimensional common latent phenotypes that are likely to give rise to numerous observed field phenotypes without relying on prior biological knowledge. The inferred genomic latent factor structure from the Bayesian network provides insights for plant breeding to simultaneously improve multiple traits, as an intervention on one trait will affect the values of focal phenotypes in an interrelated complex trait system.

scholarly journals Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes

2018 ◽  
Author(s):  
Haipeng Yu ◽  
Malachy T. Campbell ◽  
Qi Zhang ◽  
Harkamal Walia ◽  
Gota Morota
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Bayesian Network ◽  
Flowering Time ◽  
Latent Variables ◽  
Complex Traits ◽  
Latent Variable ◽  
Grain Morphology ◽  
Hill Climbing ◽  
Confirmatory Factor

AbstractWith the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multitrait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms (i.e., Hill Climbing, Tabu, Max-Min Hill Climbing, and General 2-Phase Restricted Maximization algorithms). Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the interrelated complex traits systems.

CAPABILITY MEASURES FOR m -DEPENDENT STATIONARY PROCESSES

Statistics ◽  
2003 ◽  
Vol 37 (1) ◽  
pp. 1-24 ◽  
Author(s):  
SY-MIEN CHEN ◽  
YU-SHENG HSU ◽  
W. L. PEARN
Keyword(s):  
Stationary Processes

Probability-Based and Measurement-Related Hypotheses With Full Restriction for Investigations by Means of Confirmatory Factor Analysis

Methodology ◽  
2011 ◽  
Vol 7 (4) ◽  
pp. 157-164
Author(s):  
Karl Schweizer
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Cognitive Processing ◽  
Latent Variables ◽  
Repeated Measures ◽  
Latent Variable ◽  
Model Fit ◽  
Repeated Measures Data ◽  
Confirmatory Factor ◽  
And Performance

Probability-based and measurement-related hypotheses for confirmatory factor analysis of repeated-measures data are investigated. Such hypotheses comprise precise assumptions concerning the relationships among the true components associated with the levels of the design or the items of the measure. Measurement-related hypotheses concentrate on the assumed processes, as, for example, transformation and memory processes, and represent treatment-dependent differences in processing. In contrast, probability-based hypotheses provide the opportunity to consider probabilities as outcome predictions that summarize the effects of various influences. The prediction of performance guided by inexact cues serves as an example. In the empirical part of this paper probability-based and measurement-related hypotheses are applied to working-memory data. Latent variables according to both hypotheses contribute to a good model fit. The best model fit is achieved for the model including latent variables that represented serial cognitive processing and performance according to inexact cues in combination with a latent variable for subsidiary processes.

Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

2019 ◽  
Vol 50 (1) ◽  
pp. 24-37
Author(s):  
Ben Porter ◽  
Camilla S. Øverup ◽  
Julie A. Brunson ◽  
Paras D. Mehta
Keyword(s):  
Structural Equation Modeling ◽  
Social Relations ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Complete Understanding ◽  
Social Relations Model ◽  
Flexible Framework ◽  
Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

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