I. Althöfer: On Pathology in Game Tree and Other Recursion Tree Models

ICGA Journal ◽  
1992 ◽  
Vol 15 (2) ◽  
pp. 80-80 ◽  
Author(s):  
Victor Allis
Keyword(s):  
Game Tree ◽  
Tree Models ◽  
Recursion Tree

Tree models of aggregation: multiple particle and bond-types

1983 ◽  
Vol 387 (1793) ◽  
pp. 351-365 ◽  
Keyword(s):  
Branching Processes ◽  
Binomial Theorem ◽  
Tree Structures ◽  
Aggregate Distribution ◽  
Aggregation Processes ◽  
Tree Models ◽  
Molecular Masses ◽  
Labelled Trees ◽  
Recursion Tree ◽  
Multiple Particle

This paper considers aggregation processes where particles bond pairwise to form tree-structures. The tree-structures may model chemical polymerization, the precipitin reaction or cell-surface aggregates in immunology, or rouleaux formation in haematology, among other applications. We allow multiple particle and bond-types. Bonding may be directed or undirected. Branching processes specify the aggregate distribution. A theorem for multi-type branching processes gives complete criteria for the existence of an infinite aggregate (a gel). The distribution of bonding in the gel gives the elastic properties of a chemical gel. We use the binomial theorem to extend Good’s (1960) multivariable generalization of Lagrange’s expansion. The extension gives the distribution of finite aggregates (the sol) whether or not a gel is present. Generating-function relations give the mole- and weight-average relative molecular masses of the sol. When certain equireactivity conditions hold, the sol distribution is determined by a combinatorial recursion. Tree models of aggregation require enumeration of labelled trees by partition. Branching processes provide an efficient solution to the problem. Models that incorporate rings into the trees require enumeration by partition of other graphical structures. Whittle’s (1980) enumeration of pseudomultigraphs by partition is not an appropriate model of ringformation for polymer chemistry but Gordon et al . (1971) suggest another model. The model is equivalent to enumerating graphs by partition, and is still an open problem.

A Hierarchy of Multinomial Models for Multidimensional Source Monitoring

Methodology ◽  
2005 ◽  
Vol 1 (1) ◽  
pp. 2-17 ◽  
Author(s):  
Thorsten Meiser
Keyword(s):  
Cognitive Processes ◽  
Source Monitoring ◽  
Source Information ◽  
Model Comparisons ◽  
Storage And Retrieval ◽  
Multinomial Processing Tree ◽  
Multinomial Models ◽  
Hierarchical Relationship ◽  
Statistical Framework ◽  
Tree Models

Abstract. Several models have been proposed for the measurement of cognitive processes in source monitoring. They are specified within the statistical framework of multinomial processing tree models and differ in their assumptions on the storage and retrieval of multidimensional source information. In the present article, a hierarchical relationship is demonstrated between multinomial models for crossed source information ( Meiser & Bröder, 2002 ), for partial source memory ( Dodson, Holland, & Shimamura, 1998 ) and for several sources ( Batchelder, Hu, & Riefer, 1994 ). The hierarchical relationship allows model comparisons and facilitates the specification of identifiability conditions. Conditions for global identifiability are discussed, and model comparisons are illustrated by reanalyses and by a new experiment on the storage and retrieval of multidimensional source information.

Analyzing Stochastic Dependence of Cognitive Processes in Multidimensional Source Recognition

2014 ◽  
Vol 61 (5) ◽  
pp. 402-415 ◽  
Author(s):  
Thorsten Meiser
Keyword(s):  
Cognitive Processes ◽  
Model Complexity ◽  
Multinomial Model ◽  
Stochastic Dependence ◽  
New Model ◽  
Multinomial Processing Tree ◽  
Cognitive States ◽  
The Family ◽  
Tree Models ◽  
Flexible Framework

Stochastic dependence among cognitive processes can be modeled in different ways, and the family of multinomial processing tree models provides a flexible framework for analyzing stochastic dependence among discrete cognitive states. This article presents a multinomial model of multidimensional source recognition that specifies stochastic dependence by a parameter for the joint retrieval of multiple source attributes together with parameters for stochastically independent retrieval. The new model is equivalent to a previous multinomial model of multidimensional source memory for a subset of the parameter space. An empirical application illustrates the advantages of the new multinomial model of joint source recognition. The new model allows for a direct comparison of joint source retrieval across conditions, it avoids statistical problems due to inflated confidence intervals and does not imply a conceptual imbalance between source dimensions. Model selection criteria that take model complexity into account corroborate the new model of joint source recognition.

Rising Stars and Underdogs: The Role Race and Parental Education Play in Predicting Mentorship

2021 ◽  
pp. 0044118X2110046
Author(s):  
Veronica Fruiht ◽  
Jordan Boeder ◽  
Thomas Chan
Keyword(s):  
Critical Race Theory ◽  
Parental Education ◽  
Individual Characteristics ◽  
Conditional Inference ◽  
Youth Of Color ◽  
Social Success ◽  
Conditional Inference Tree ◽  
Tree Models ◽  
Nationally Representative ◽  
Inference Tree

Research suggests that youth with more financial and social resources are more likely to have access to mentorship. Conversely, the rising star hypothesis posits that youth who show promise through their individual successes are more likely to be mentored. Utilizing a nationally representative sample ( N = 4,882), we tested whether demographic characteristics (e.g., race, SES) or personal resources (e.g., academic/social success) are better predictors of receiving mentorship. Regression analyses suggested that demographic, contextual, and individual characteristics all significantly predicted access to mentorship, specifically by non-familial mentors. However, conditional inference tree models that explored the interaction of mentorship predictors by race showed that individual characteristics mattered less for Black and Latino/a youth. Therefore, the rising star hypothesis may hold true for White youth, but the story of mentoring is more complicated for youth of color. Findings highlight the implications of Critical Race Theory for mentoring research and practice.

scholarly journals CPT to RVU conversion improves model performance in the prediction of surgical case length

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicholas Garside ◽  
Hamed Zaribafzadeh ◽  
Ricardo Henao ◽  
Royce Chung ◽  
Daniel Buckland
Keyword(s):  
Model Performance ◽  
Individual Case ◽  
Model Complexity ◽  
Actual Case ◽  
Surgical Case ◽  
Relative Value ◽  
Prediction Time ◽  
Boosted Decision Tree ◽  
Tree Models ◽  
Duke University

AbstractMethods used to predict surgical case time often rely upon the current procedural terminology (CPT) code as a nominal variable to train machine-learned models, however this limits the ability of the model to incorporate new procedures and adds complexity as the number of unique procedures increases. The relative value unit (RVU, a consensus-derived billing indicator) can serve as a proxy for procedure workload and could replace the CPT code as a primary feature for models that predict surgical case length. Using 11,696 surgical cases from Duke University Health System electronic health records data, we compared boosted decision tree models that predict individual case length, changing the method by which the model coded procedure type; CPT, RVU, and CPT–RVU combined. Performance of each model was assessed by inference time, MAE, and RMSE compared to the actual case length on a test set. Models were compared to each other and to the manual scheduler method that currently exists. RMSE for the RVU model (60.8 min) was similar to the CPT model (61.9 min), both of which were lower than scheduler (90.2 min). 65.2% of our RVU model’s predictions (compared to 43.2% from the current human scheduler method) fell within 20% of actual case time. Using RVUs reduced model prediction time by ninefold and reduced the number of training features from 485 to 44. Replacing pre-operative CPT codes with RVUs maintains model performance while decreasing overall model complexity in the prediction of surgical case length.

scholarly journals Significance of pepsinogen in screening for gastric intestinal metaplasia in Guangdong, China

2021 ◽  
Vol 49 (2) ◽  
pp. 030006052199049
Author(s):  
Xujuan Liu ◽  
Min Zhang ◽  
Riyu Luo ◽  
Keran Mo ◽  
Xingxiang He
Keyword(s):  
Logistic Regression ◽  
Intestinal Metaplasia ◽  
Single Factor ◽  
Antibody Testing ◽  
Cross Sectional ◽  
Gastric Intestinal Metaplasia ◽  
Risk Detection ◽  
Tree Models ◽  
Single Factor Analysis ◽  
Serological Biomarkers

Objective Diagnosis of gastric intestinal metaplasia (GIM) relies on gastroscopy and histopathologic biopsy, but their application in screening for GIM is limited. We aimed to identify serological biomarkers of GIM via screening in Guangdong, China. Methods Cross-sectional field and questionnaire data, demographic information, past medical history, and other relevant data were collected. Blood samples were collected for pepsinogen (PG)I, PGII, gastrin-17, and Helicobacter pylori antibody testing, and gastroscopy and histopathologic biopsy were performed. Single factor and logistic regression analyses were used to evaluate the correlation between these indicators and GIM, and decision tree models were used to determine the cut-off points between indicators. Results Of 443 participants enrolled, 87 (19.6%) were diagnosed with GIM. Single factor analysis showed that pepsin indicators (PGI, PGII, and PGI/PGII ratio) and the factors Mandarin as native language, urban residency, hyperlipidemia, and age were associated with GIM. Logistic regression analysis showed that PGI and age were associated with GIM. Conclusions Age is an important factor for predicting GIM progression; age >60 years increased its risk. Detection of GIM was higher in individuals with PGI levels >127.20 ng/mL, which could be used as a threshold indicating the need to perform gastroscopy and histopathologic biopsy.

scholarly journals ENTMOOT: A Framework for Optimization over Ensemble Tree Models

2021 ◽  
pp. 107343
Author(s):  
Alexander Thebelt ◽  
Jan Kronqvist ◽  
Miten Mistry ◽  
Robert M. Lee ◽  
Nathan Sudermann-Merx ◽  
...  
Keyword(s):  
Tree Models
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