A graphical model of procedures for an automated manager's assistant

1982 ◽  
Author(s):  
Michael Bauer ◽  
Sylvia Osborn
Keyword(s):  
Graphical Model

DEVELOPMENT OF A METHOD FOR DESIGNING INFORMATION SYSTEMS FOR ENERGY COMPANIES

2020 ◽  
Vol 5 (333) ◽  
pp. 53-57
Author(s):  
T.B. Aldongar ◽  
◽  
F.U. Malikova ◽  
G.B. Issayeva ◽  
B.R. Absatarova ◽  
...  
Keyword(s):  
Information Systems ◽  
Business Processes ◽  
Design Research ◽  
Graphical Model ◽  
Formal Model ◽  
Workflow Management ◽  
Research Process ◽  
Information Models ◽  
The Creation ◽  
Concept Of Information

The creation of information models requires the use of known methods and the development of new methods of formalizing the pre-design research process. The modeling process consists of four stages: data collection on the object of management - pre-project research; creation of a graphical model of business processes taking place in the enterprise; development of a formal model of business processes; business research by optimizing the formal model. To support the creation of workflow management services and systems, the complex offers methodologies, standards and specialized software that make up the developer's tools. This can be ensured only by modern automated methods based on information systems. It is important that the information collected is structured to meet the needs of potential users and stored in a form that allows the use of modern access technologies. Before discussing the effectiveness of FIM, it should be noted that the basic concept of information itself is still not the same. In a pragmatic way, it is a set of messages in the form of an important document for the system. Information can be evaluated not only by volume, but also by various parameters, the most important of which are: timeliness, relevance, value, aging, accuracy, etc. in addition, the information may be clear, probable and accurate. The methods of its reception and processing are different in each case.

A Probabilistic-Graphical-Model Based Approach for Representing Lineages in Uncertain Data

2011 ◽  
Vol 34 (10) ◽  
pp. 1897-1906 ◽  
Author(s):  
Kun YUE ◽  
Wei-Yi LIU ◽  
Yun-Lei ZHU ◽  
Wei ZHANG
Keyword(s):  
Graphical Model ◽  
Uncertain Data ◽  
Probabilistic Graphical Model ◽  
Model Based

Psychological networks in clinical populations: investigating the consequences of Berkson's bias

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jill de Ron ◽  
Eiko I. Fried ◽  
Sacha Epskamp
Keyword(s):  
Network Structure ◽  
Binary Data ◽  
Ordinal Data ◽  
Graphical Model ◽  
Rating Scale ◽  
Network Models ◽  
Simulation Studies ◽  
Potential Threat ◽  
Sum Score ◽  
Research Populations

Abstract Background In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson's bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson's bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data. Methods In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants. Results The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson's bias literature, selection reduced recovery rates by inducing negative connections between the items. Conclusion Our findings provide evidence that Berkson's bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson's bias and their pitfalls.

scholarly journals Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 311
Author(s):  
Zhenqiu Liu
Keyword(s):  
Single Cell ◽  
Free Parameter ◽  
Graphical Model ◽  
Expression Patterns ◽  
Information Criterion ◽  
Log P ◽  
Rna Seq ◽  
Clustering Methods ◽  
Wide Range ◽  
Free Parameters

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

scholarly journals How does music performance anxiety relate to other anxiety disorders?

2021 ◽  
pp. 030573562098860
Author(s):  
Anna Wiedemann ◽  
Daniel Vogel ◽  
Catharina Voss ◽  
Jana Hoyer
Keyword(s):  
Cluster Analysis ◽  
Anxiety Disorder ◽  
Anxiety Disorders ◽  
Graphical Model ◽  
Music Performance ◽  
Performance Anxiety ◽  
Separation Anxiety Disorder ◽  
Systematic Analysis ◽  
Music Performance Anxiety ◽  
Dsm 5

Music performance anxiety (MPA) is considered a social anxiety disorder (SAD). Recent conceptualizations, however, challenge existing MPA definitions, distinguishing MPA from SAD. In this study, we aim to provide a systematic analysis of MPA interdependencies to other anxiety disorders through graphical modeling and cluster analysis. Participants were 82 music students ( Mage = 23.5 years, SD = 3.4 years; 69.5% women) with the majority being vocal (30.5%), string (24.4%), or piano (19.5%) students. MPA was measured using the German version of the Kenny Music Performance Anxiety Inventory (K-MPAI). All participants were tested for anxiety-related symptoms using the disorder-specific anxiety measures of the Diagnostic and Statistical Manual of Mental Disorders (5th ed., DSM-5), including agoraphobia (AG), generalized anxiety disorder (GAD), panic disorder (PD), separation anxiety disorder (SEP), specific phobia (SP), SAD, and illness anxiety disorder (ILL). We found no evidence of MPA being primarily connected to SAD, finding GAD acted as a full mediator between MPA and any other anxiety type. Our graphical model remained unchanged considering severe cases of MPA only (K-MPAI ⩾ 105). By means of cluster analysis, we identified two participant sub-groups of differing anxiety profiles. Participants with pathological anxiety consistently showed more severe MPA. Our findings suggest that GAD is the strongest predictor for MPA among all major DSM-5 anxiety types.

A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints

2015 ◽  
Vol 43 (1) ◽  
pp. 267-281 ◽  
Author(s):  
Nikita Mishra ◽  
Huazhe Zhang ◽  
John D. Lafferty ◽  
Henry Hoffmann
Keyword(s):  
Graphical Model ◽  
Probabilistic Graphical Model ◽  
Performance Constraints ◽  
Model Based

scholarly journals Classification of unlabeled online media

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sakthi Kumar Arul Prakash ◽  
Conrad Tucker
Keyword(s):  
Social Media ◽  
Real World ◽  
Graphical Model ◽  
Ground Truth ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Social Media Networks ◽  
Online Social Media ◽  
Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

scholarly journals A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 875
Author(s):  
Jesus Cerquides ◽  
Mehmet Oğuz Mülâyim ◽  
Jerónimo Hernández-González ◽  
Amudha Ravi Shankar ◽  
Jose Luis Fernandez-Marquez
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Graphical Model ◽  
Real Life ◽  
Scientific Journals ◽  
Probabilistic Framework ◽  
Science Data ◽  
Label Aggregation ◽  
Evaluation Of Data ◽  
Model Formalism

Over the last decade, hundreds of thousands of volunteers have contributed to science by collecting or analyzing data. This public participation in science, also known as citizen science, has contributed to significant discoveries and led to publications in major scientific journals. However, little attention has been paid to data quality issues. In this work we argue that being able to determine the accuracy of data obtained by crowdsourcing is a fundamental question and we point out that, for many real-life scenarios, mathematical tools and processes for the evaluation of data quality are missing. We propose a probabilistic methodology for the evaluation of the accuracy of labeling data obtained by crowdsourcing in citizen science. The methodology builds on an abstract probabilistic graphical model formalism, which is shown to generalize some already existing label aggregation models. We show how to make practical use of the methodology through a comparison of data obtained from different citizen science communities analyzing the earthquake that took place in Albania in 2019.

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