scholarly journals Bayesian Hierarchical Model-Based Information Fusion for Degradation Analysis Considering Non-Competing Relationship

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 175222-175227
Author(s):  
Junyu Guo ◽  
Hualin Zheng ◽  
Binglin Li ◽  
Guo-Zhong Fu
Keyword(s):  
Information Fusion ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical ◽  
Model Based ◽  
Degradation Analysis

scholarly journals A Bayesian model integration for mutation calling through data partitioning

2019 ◽  
Vol 35 (21) ◽  
pp. 4247-4254 ◽  
Author(s):  
Takuya Moriyama ◽  
Seiya Imoto ◽  
Shuto Hayashi ◽  
Yuichi Shiraishi ◽  
Satoru Miyano ◽  
...  
Keyword(s):  
Hierarchical Model ◽  
Bayesian Model ◽  
Information Sources ◽  
Tumor Model ◽  
Bayesian Hierarchical Model ◽  
Generative Models ◽  
Error Model ◽  
Model Integration ◽  
Bayesian Hierarchical ◽  
Model Based

Abstract Motivation Detection of somatic mutations from tumor and matched normal sequencing data has become among the most important analysis methods in cancer research. Some existing mutation callers have focused on additional information, e.g. heterozygous single-nucleotide polymorphisms (SNPs) nearby mutation candidates or overlapping paired-end read information. However, existing methods cannot take multiple information sources into account simultaneously. Existing Bayesian hierarchical model-based methods construct two generative models, the tumor model and error model, and limited information sources have been modeled. Results We proposed a Bayesian model integration framework named as partitioning-based model integration. In this framework, through introducing partitions for paired-end reads based on given information sources, we integrate existing generative models and utilize multiple information sources. Based on that, we constructed a novel Bayesian hierarchical model-based method named as OHVarfinDer. In both the tumor model and error model, we introduced partitions for a set of paired-end reads that cover a mutation candidate position, and applied a different generative model for each category of paired-end reads. We demonstrated that our method can utilize both heterozygous SNP information and overlapping paired-end read information effectively in simulation datasets and real datasets. Availability and implementation https://github.com/takumorizo/OHVarfinDer. Supplementary information Supplementary data are available at Bioinformatics online.

Bayesian hierarchical model-based prognostics for lithium-ion batteries

2018 ◽  
Vol 172 ◽  
pp. 25-35 ◽  
Author(s):  
Madhav Mishra ◽  
Jesper Martinsson ◽  
Matti Rantatalo ◽  
Kai Goebel
Keyword(s):  
Lithium Ion Batteries ◽  
Hierarchical Model ◽  
Lithium Ion ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical ◽  
Model Based

scholarly journals Measuring spatial allocative efficiency in basketball

2020 ◽  
Vol 16 (4) ◽  
pp. 271-289
Author(s):  
Nathan Sandholtz ◽  
Jacob Mortensen ◽  
Luke Bornn
Keyword(s):  
Spatial Distribution ◽  
Hierarchical Model ◽  
Opportunity Cost ◽  
Allocative Efficiency ◽  
Bayesian Hierarchical Model ◽  
National Basketball Association ◽  
Spatial Context ◽  
Bayesian Hierarchical ◽  
The Impact

AbstractEvery shot in basketball has an opportunity cost; one player’s shot eliminates all potential opportunities from their teammates for that play. For this reason, player-shot efficiency should ultimately be considered relative to the lineup. This aspect of efficiency—the optimal way to allocate shots within a lineup—is the focus of our paper. Allocative efficiency should be considered in a spatial context since the distribution of shot attempts within a lineup is highly dependent on court location. We propose a new metric for spatial allocative efficiency by comparing a player’s field goal percentage (FG%) to their field goal attempt (FGA) rate in context of both their four teammates on the court and the spatial distribution of their shots. Leveraging publicly available data provided by the National Basketball Association (NBA), we estimate player FG% at every location in the offensive half court using a Bayesian hierarchical model. Then, by ordering a lineup’s estimated FG%s and pairing these rankings with the lineup’s empirical FGA rate rankings, we detect areas where the lineup exhibits inefficient shot allocation. Lastly, we analyze the impact that sub-optimal shot allocation has on a team’s overall offensive potential, demonstrating that inefficient shot allocation correlates with reduced scoring.

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