Application of a Bayesian hierarchical model to estimate trends in Atlantic harbor seal ( Phoca vitulina vitulina ) abundance in Maine, U.S.A., 1993–2018

2021 ◽  
Author(s):  
Douglas B. Sigourney ◽  
Kimberly T. Murray ◽  
James R. Gilbert ◽  
Jay M. Ver Hoef ◽  
Elizabeth Josephson ◽  
...  
Keyword(s):  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Harbor Seal ◽  
Phoca Vitulina ◽  
Bayesian Hierarchical

Bayesian Hierarchical Model Characterization of Model Error in Ocean Data Assimilation and Forecasts

2012 ◽  
Author(s):  
Ralph F. Milliff ◽  
Christopher K. Wikle ◽  
L. M. Berliner ◽  
Radu Herbei
Keyword(s):  
Data Assimilation ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Model Error ◽  
Bayesian Hierarchical ◽  
Ocean Data Assimilation

scholarly journals A Bayesian Hierarchical Model for Comparing Average F1 Scores

2015 ◽  
Author(s):  
Dell Zhang ◽  
Jun Wang ◽  
Xiaoxue Zhao ◽  
Xiaoling Wang
Keyword(s):  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical

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.

A point-based Bayesian hierarchical model to predict the outcome of tennis matches

2019 ◽  
Vol 15 (4) ◽  
pp. 313-325 ◽  
Author(s):  
Martin Ingram
Keyword(s):  
Random Walk ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical ◽  
Gaussian Random Walk ◽  
Over Time ◽  
Modelling Approach ◽  
Independent And Identically Distributed

Abstract A well-established assumption in tennis is that point outcomes on each player’s serve in a match are independent and identically distributed (iid). With this assumption, it is enough to specify the serve probabilities for both players to derive a wide variety of event distributions, such as the expected winner and number of sets, and number of games. However, models using this assumption, which we will refer to as “point-based”, have typically performed worse than other models in the literature at predicting the match winner. This paper presents a point-based Bayesian hierarchical model for predicting the outcome of tennis matches. The model predicts the probability of winning a point on serve given surface, tournament and match date. Each player is given a serve and return skill which is assumed to follow a Gaussian random walk over time. In addition, each player’s skill varies by surface, and tournaments are given tournament-specific intercepts. When evaluated on the ATP’s 2014 season, the model outperforms other point-based models, predicting match outcomes with greater accuracy (68.8% vs. 66.3%) and lower log loss (0.592 vs. 0.641). The results are competitive with approaches modelling the match outcome directly, demonstrating the forecasting potential of the point-based modelling approach.

Bayesian Hierarchical Model for Change Point Detection in Multivariate Sequences

Technometrics ◽  
2021 ◽  
pp. 1-23
Author(s):  
Huaqing Jin ◽  
Guosheng Yin ◽  
Binhang Yuan ◽  
Fei Jiang
Keyword(s):  
Hierarchical Model ◽  
Change Point ◽  
Bayesian Hierarchical Model ◽  
Change Point Detection ◽  
Bayesian Hierarchical ◽  
Point Detection
Sign in / Sign up

Export Citation Format

Share Document