scholarly journals Why does the Dyad-4PNO model of Kern and Culpepper (2020) fit real data?

2020 ◽  
Author(s):  
Gunter Maris ◽  
Timo Bechger ◽  
Benjamin Deonovic
Keyword(s):  
Simple Proof ◽  
Real Data ◽  
Psychological Measurement ◽  
Tree Model ◽  
Latent Tree Model

This note aims to elucidate why the Dyad-4PNO model of Kern and Culpepper (2020) can be expected to fit real data reasonably well. The main result is that the Dyad-4PNO approximates a latent tree model. We offer a simple proof of identifiability, and draw some implications for psychological measurement in practice.

scholarly journals Latent Tree Models and Approximate Inference in Bayesian Networks

2008 ◽  
Vol 32 ◽  
pp. 879-900 ◽  
Author(s):  
Y. Wang ◽  
N. L. Zhang ◽  
T. Chen
Keyword(s):  
Bayesian Networks ◽  
Linear Time ◽  
Computational Cost ◽  
Approximate Inference ◽  
Tree Model ◽  
Approximation Accuracy ◽  
Latent Tree Models ◽  
Tree Models ◽  
Novel Method ◽  
Latent Tree Model

We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and when online, make inference with the LTM instead of the original BN. Because LTMs are tree-structured, inference takes linear time. In the meantime, they can represent complex relationship among leaf nodes and hence the approximation accuracy is often good. Empirical evidence shows that our method can achieve good approximation accuracy at low online computational cost.

scholarly journals Detection of arterial wall abnormalities via Bayesian model selection

2018 ◽  
Author(s):  
Karen Larson ◽  
Clark Bowman ◽  
Costas Papadimitriou ◽  
Petros Koumoutsakos ◽  
Anastasios Matzavinos
Keyword(s):  
Parameter Estimation ◽  
Model Selection ◽  
Parallel Implementation ◽  
Real Data ◽  
Physical Models ◽  
Patient Specific ◽  
Small Scale ◽  
Tree Model ◽  
Arterial Tree ◽  
Flow Models

AbstractPatient-specific modeling of hemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter estimation for more complex forward models, and a system for practical model selection, allowing evidence-based comparison between distinct physical models. We demonstrate the proposed methodology by generating simulated noisy flow velocity data from a branching arterial tree model in which a structural defect is introduced at an unknown location; our approach is shown to accurately locate the abnormality and estimate its physical properties even in the presence of significant observational and systemic error. As the method readily admits real data, it shows great potential in patient-specific parameter fitting for hemodynamical flow models.

scholarly journals Detection of arterial wall abnormalities via Bayesian model selection

2019 ◽  
Vol 6 (10) ◽  
pp. 182229
Author(s):  
Karen Larson ◽  
Clark Bowman ◽  
Costas Papadimitriou ◽  
Petros Koumoutsakos ◽  
Anastasios Matzavinos
Keyword(s):  
Parameter Estimation ◽  
Model Selection ◽  
Parallel Implementation ◽  
Real Data ◽  
Physical Models ◽  
Patient Specific ◽  
Small Scale ◽  
Tree Model ◽  
Arterial Tree ◽  
Flow Models

Patient-specific modelling of haemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter estimation for more complex forward models, and a system for practical model selection, allowing evidence-based comparison between distinct physical models. We demonstrate the proposed methodology by generating simulated noisy flow velocity data from a branching arterial tree model in which a structural defect is introduced at an unknown location; our approach is shown to accurately locate the abnormality and estimate its physical properties even in the presence of significant observational and systemic error. As the method readily admits real data, it shows great potential in patient-specific parameter fitting for haemodynamical flow models.

scholarly journals Polarized Classification Tree Models: Theory and Computational Aspects

2021 ◽  
Author(s):  
Elena Ballante ◽  
Marta Galvani ◽  
Pierpaolo Uberti ◽  
Silvia Figini
Keyword(s):  
Information Gain ◽  
Classification Tree ◽  
Real Data ◽  
Simulation Framework ◽  
Tree Model ◽  
New Approach ◽  
Classification Tree Model ◽  
Tree Modeling ◽  
Tree Models ◽  
Computational Aspects

AbstractIn this paper, a new approach in classification models, called Polarized Classification Tree model, is introduced. From a methodological perspective, a new index of polarization to measure the goodness of splits in the growth of a classification tree is proposed. The new introduced measure tackles weaknesses of the classical ones used in classification trees (Gini and Information Gain), because it does not only measure the impurity but it also reflects the distribution of each covariate in the node, i.e., employing more discriminating covariates to split the data at each node. From a computational prospective, a new algorithm is proposed and implemented employing the new proposed measure in the growth of a tree. In order to show how our proposal works, a simulation exercise has been carried out. The results obtained in the simulation framework suggest that our proposal significantly outperforms impurity measures commonly adopted in classification tree modeling. Moreover, the empirical evidence on real data shows that Polarized Classification Tree models are competitive and sometimes better with respect to classical classification tree models.

scholarly journals Heart Disease Prediction Integrating UMAP and XGBoost

2020 ◽  
Vol 9 (1) ◽  
pp. 2449-2457
Keyword(s):  
Heart Diseases ◽  
Real Data ◽  
Support Vector ◽  
Tree Model ◽  
Data Mining Algorithms ◽  
Tree Construction ◽  
Svm Model ◽  
Time Required ◽  
The Right ◽  
Mining Algorithms

The healthcare industry is flooded with the plethora of data about the patients which is supplemented each day in the form of medical records. Researchers have been putting in various efforts to bring this data into usage for the prediction of various diseases. Prediction of heart diseases is one such area. Data mining algorithms have been at the centre of improving the prediction of accuracy of heart diseases. But it has been found that these algorithms are not using adequate set of attributes for prediction that sometimes may lead to wrong predictions. The aim of this paper is to deploy the right set of algorithms to accurately predict the heart diseases and help both the patient and the doctor. The paper thrives to put UMAP and XGBoost techniques in this regard and exploit the advantages of both techniques. UMAP helps in dimensionality reduction without loss of useful data while XGBoost uses parallelization for tree construction reducing the time required to get the results. The experiment is carried on real data taken from Fortis Escorts, Faridabad, India. The results are compared with existing techniques such as Naïve Bayes, Decision Tree model, Logistic Regression model and Support Vector Machine (SVM) model based on various parameters such as accuracy, recall and precision. Remarkable accuracy of 94.59%, recall of 87.87, precision of 100 has been achieved.

scholarly journals Rationale for Replacement of the Destructive Test by Non-Destructive One in Medical Devices Manufacturing

2020 ◽  
Vol 68 (6) ◽  
pp. 967-972
Author(s):  
Oldřich Beneš ◽  
David Hampel
Keyword(s):  
Medical Devices ◽  
Manufacturing Industry ◽  
Classification Tree ◽  
Real Data ◽  
Destructive Testing ◽  
Tree Model ◽  
Destructive Test ◽  
Classification Tree Model ◽  
Regulated Industries ◽  
Non Destructive

Due to expanding demand for the level of testing on one side and reduction of costs on the other side, the question how to replace expensive destructive testing of medical devices without compromising the quality of final product arising urgently. This situation is common within all highly regulated industries – in this article is addressed the problem from medical device manufacturing industry. Based on real data containing testing and validation datasets, logit model and classification tree model are estimated for establishing the relationship between result of destructive test and measurements of explored device. Results point to possibility of replacing destructive test by non-destructive one in our case.

scholarly journals A Survey on Latent Tree Models and Applications

2013 ◽  
Vol 47 ◽  
pp. 157-203 ◽  
Author(s):  
R. Mourad ◽  
C. Sinoquet ◽  
N. L. Zhang ◽  
T. Liu ◽  
P. Leray
Keyword(s):  
Graphical Models ◽  
Latent Variables ◽  
Simple Structure ◽  
Tree Model ◽  
The Past ◽  
Latent Tree Models ◽  
Tree Models ◽  
Complex Relationships ◽  
Latent Tree Model ◽  
Comprehensive Study

In data analysis, latent variables play a central role because they help provide powerful insights into a wide variety of phenomena, ranging from biological to human sciences. The latent tree model, a particular type of probabilistic graphical models, deserves attention. Its simple structure - a tree - allows simple and efficient inference, while its latent variables capture complex relationships. In the past decade, the latent tree model has been subject to significant theoretical and methodological developments. In this review, we propose a comprehensive study of this model. First we summarize key ideas underlying the model. Second we explain how it can be efficiently learned from data. Third we illustrate its use within three types of applications: latent structure discovery, multidimensional clustering, and probabilistic inference. Finally, we conclude and give promising directions for future researches in this field.

Verifying TCM Syndrome Hypothesis Based on Improved Latent Tree Model

2018 ◽  
pp. 460-469
Author(s):  
Nian Zhou ◽  
Lingshan Zhou ◽  
Lili Peng ◽  
Bing Wang ◽  
Peng Chen ◽  
...  
Keyword(s):  
Tree Model ◽  
Tcm Syndrome ◽  
Latent Tree Model
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