scholarly journals Bayesian Hierarchical Model for Estimating Gene Expression Intensity Using Multiple Scanned Microarrays

2008 ◽  
Vol 2008 ◽  
pp. 1-11
Author(s):  
Rashi Gupta ◽  
Elja Arjas ◽  
Sangita Kulathinal ◽  
Andrew Thomas ◽  
Petri Auvinen
Keyword(s):  
Gene Expression ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical ◽  
Expression Intensity ◽  
Gene Expression Intensity

scholarly journals Bayesian Hierarchical Model for Differential Gene Expression Using RNA-Seq Data

2013 ◽  
Vol 7 (1) ◽  
pp. 48-67 ◽  
Author(s):  
Juhee Lee ◽  
Yuan Ji ◽  
Shoudan Liang ◽  
Guoshuai Cai ◽  
Peter Müller
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Rna Seq ◽  
Bayesian Hierarchical ◽  
Differential Gene

scholarly journals Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data

2007 ◽  
Vol 8 (1) ◽  
pp. 283 ◽  
Author(s):  
Xiangdong Liu ◽  
Walter J Jessen ◽  
Siva Sivaganesan ◽  
Bruce J Aronow ◽  
Mario Medvedovic
Keyword(s):  
Gene Expression ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical ◽  
Chip Data ◽  
Transcriptional Module ◽  
Module Discovery ◽  
Chip Chip

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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