scholarly journals A First Approach to Closeness Distributions

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3112
Author(s):  
Jesus Cerquides
Keyword(s):  
Probability Distribution ◽  
Graphical Models ◽  
Hierarchical Models ◽  
Geometric Approach ◽  
Probabilistic Graphical Models ◽  
Formal Definition ◽  
Large Probability ◽  
Definition Of

Probabilistic graphical models allow us to encode a large probability distribution as a composition of smaller ones. It is oftentimes the case that we are interested in incorporating in the model the idea that some of these smaller distributions are likely to be similar to one another. In this paper we provide an information geometric approach on how to incorporate this information and see that it allows us to reinterpret some already existing models. Our proposal relies on providing a formal definition of what it means to be close. We provide an example on how this definition can be actioned for multinomial distributions. We use the results on multinomial distributions to reinterpret two already existing hierarchical models in terms of closeness distributions.

Learning probabilistic networks

1999 ◽  
Vol 13 (4) ◽  
pp. 321-351 ◽  
Author(s):  
PAUL J. KRAUSE
Keyword(s):  
Probability Theory ◽  
Probability Distribution ◽  
Prior Knowledge ◽  
Graphical Models ◽  
Incomplete Data ◽  
Graphical Model ◽  
Probabilistic Graphical Models ◽  
Short Discussion ◽  
Probabilistic Networks ◽  
Probabilistic Network

A probabilistic network is a graphical model that encodes probabilistic relationships between variables of interest. Such a model records qualitative influences between variables in addition to the numerical parameters of the probability distribution. As such it provides an ideal form for combining prior knowledge, which might be limited solely to experience of the influences between some of the variables of interest, and data. In this paper, we first show how data can be used to revise initial estimates of the parameters of a model. We then progress to showing how the structure of the model can be revised as data is obtained. Techniques for learning with incomplete data are also covered. In order to make the paper as self contained as possible, we start with an introduction to probability theory and probabilistic graphical models. The paper concludes with a short discussion on how these techniques can be applied to the problem of learning causal relationships between variables in a domain of interest.

scholarly journals Students’ Understanding on Formal Definition of Limit

2021 ◽  
Vol 1752 (1) ◽  
pp. 012082
Author(s):  
Nurdin ◽  
S F Assagaf ◽  
F Arwadi
Keyword(s):  
Formal Definition ◽  
Definition Of

Criteria for Accrediting Expert Wine Judges

2013 ◽  
Vol 9 (1) ◽  
pp. 62-74 ◽  
Author(s):  
Robert Hodgson ◽  
Jing Cao
Keyword(s):  
Probability Distribution ◽  
Wine Industry ◽  
Ordinal Scale ◽  
Professional Experience ◽  
Wine Quality ◽  
Basic Premise ◽  
Definition Of ◽  
Jel Classifications

AbstractA test for evaluating wine judge performance is developed. The test is based on the premise that an expert wine judge will award similar scores to an identical wine. The definition of “similar” is parameterized to include varying numbers of adjacent awards on an ordinal scale, from No Award to Gold. For each index of similarity, a probability distribution is developed to determine the likelihood that a judge might pass the test by chance alone. When the test is applied to the results from a major wine competition, few judges pass the test. Of greater interest is that many judges who fail the test have vast professional experience in the wine industry. This leads to us to question the basic premise that experts are able to provide consistent evaluations in wine competitions and, hence, that wine competitions do not provide reliable recommendations of wine quality. (JEL Classifications: C02, C12, D81)

Diagnosis Rule Discovery Based on Causality in the Context of Fault Diagnosis in Rotating Machinery

2014 ◽  
Vol 532 ◽  
pp. 113-117
Author(s):  
Zhou Jin ◽  
Ru Jing Wang ◽  
Jie Zhang
Keyword(s):  
Fault Diagnosis ◽  
General Framework ◽  
Complex Structure ◽  
Rotating Machinery ◽  
Massive Data ◽  
Formal Definition ◽  
Rule Discovery ◽  
Rule Based ◽  
Simple Implementation ◽  
Definition Of

The rotating machineries in a factory usually have the characteristics of complex structure and highly automated logic, which generated a large amounts of monitoring data. It is an infeasible task for uses to deal with the massive data and locate fault timely. In this paper, we explore the causality between symptom and fault in the context of fault diagnosis in rotating machinery. We introduce data mining into fault diagnosis and provide a formal definition of causal diagnosis rule based on statistic test. A general framework for diagnosis rule discovery based on causality is provided and a simple implementation is explored with the purpose of providing some enlightenment to the application of causality discovery in fault diagnosis of rotating machinery.

scholarly journals Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings

2021 ◽  
Author(s):  
Arjun P. Athreya ◽  
Tanja Brückl ◽  
Elisabeth B. Binder ◽  
A. John Rush ◽  
Joanna Biernacka ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Graphical Models ◽  
Selective Serotonin Reuptake Inhibitors ◽  
Probabilistic Graphical Models ◽  
Sociodemographic Factors ◽  
Response To Therapy ◽  
Antidepressant Response ◽  
Symptom Profiles ◽  
Poor Outcomes ◽  
Multiple Drugs

AbstractHeterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians’ ability to accurately predict a specific patient’s eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming.

scholarly journals Time to Harmonize Dengue Nomenclature and Classification

Viruses ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 569 ◽  
Author(s):  
Lize Cuypers ◽  
Pieter Libin ◽  
Peter Simmonds ◽  
Ann Nowé ◽  
Jorge Muñoz-Jordán ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Dengue Virus ◽  
Classification System ◽  
Formal Definition ◽  
Classification Procedure ◽  
Genotype 2 ◽  
Global Spread ◽  
Significant Burden ◽  
Definition Of ◽  
Epidemiological Characteristics

Dengue virus (DENV) is estimated to cause 390 million infections per year worldwide. A quarter of these infections manifest clinically and are associated with a morbidity and mortality that put a significant burden on the affected regions. Reports of increased frequency, intensity, and extended geographical range of outbreaks highlight the virus’s ongoing global spread. Persistent transmission in endemic areas and the emergence in territories formerly devoid of transmission have shaped DENV’s current genetic diversity and divergence. This genetic layout is hierarchically organized in serotypes, genotypes, and sub-genotypic clades. While serotypes are well defined, the genotype nomenclature and classification system lack consistency, which complicates a broader analysis of their clinical and epidemiological characteristics. We identify five key challenges: (1) Currently, there is no formal definition of a DENV genotype; (2) Two different nomenclature systems are used in parallel, which causes significant confusion; (3) A standardized classification procedure is lacking so far; (4) No formal definition of sub-genotypic clades is in place; (5) There is no consensus on how to report antigenic diversity. Therefore, we believe that the time is right to re-evaluate DENV genetic diversity in an essential effort to provide harmonization across DENV studies.

Extended Probability Trees for Probabilistic Graphical Models

2014 ◽  
pp. 113-128
Author(s):  
Andrés Cano ◽  
Manuel Gómez-Olmedo ◽  
Serafín Moral ◽  
Cora B. Pérez-Ariza
Keyword(s):  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
Probability Trees
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