scholarly journals A stage-structured hierarchical Bayes model for the babassu palm tree population dynamics – Estimated from anthropogenic open area data sets

2019 ◽  
Vol 400 ◽  
pp. 14-26
Author(s):  
Nikolay Sirakov ◽  
Bénédicte Fontez ◽  
Thérèse Libourel ◽  
Alessio dos Santos ◽  
Danielle Mitja ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Hierarchical Bayes ◽  
Data Sets ◽  
Palm Tree ◽  
Open Area ◽  
Hierarchical Bayes Model ◽  
Bayes Model ◽  
Tree Population ◽  
Babassu Palm ◽  
Area Data

Estimating Irregular Pricing Effects: A Stochastic Spline Regression Approach

1998 ◽  
Vol 35 (1) ◽  
pp. 16-29 ◽  
Author(s):  
Kirthi Kalyanam ◽  
Thomas S. Shively
Keyword(s):  
Hierarchical Bayes ◽  
Data Sets ◽  
Response Functions ◽  
Spline Regression ◽  
Price Response ◽  
Product Categories ◽  
Hierarchical Bayes Model ◽  
Pricing Decisions ◽  
Bayes Model ◽  
Regression Approach

Markets respond to prices in complex ways. Multiple factors such as price points, odd pricing, and just-noticeable differences often cause steps and spikes in response. The result is market response functions that are frequently nonmonotonic. However, existing regression-based approaches employ functions that are inherently monotonic, which thereby limits representation of important irregularities. In this article, the authors use a stochastic spline regression approach in the framework of a hierarchical Bayes model that permits the estimation of irregular pricing effects and apply the approach to data sets from several product categories. A simulation study indicates that the stochastic spline approach is flexible enough to accommodate irregular response functions. The empirical results show that there are irregularities in own-price response for most of the brands examined and that there are important profit implications of these irregular response functions in pricing decisions. The authors find that the irregularities in the response functions include sales increases associated with odd prices in the range of 12% to 76%, flatness at the extremes of the range of observed prices, and kinks in the response function that are consistent with segmentation effects.

How patrons value casino promotional offers: A conjoint study

2019 ◽  
Vol 26 (4) ◽  
pp. 640-657
Author(s):  
Mark Legg ◽  
Murat Hancer
Keyword(s):  
Conjoint Analysis ◽  
Hierarchical Bayes ◽  
Hierarchical Bayes Model ◽  
Bayes Model ◽  
Visitation Frequency ◽  
Food And Beverage ◽  
Negative Role

Casinos rely extensively on free slot play (FSP) offers for incentivizing patron visitation. However, there has been a lack of understanding its influence on driving patron visitation and patrons’ valuation of FSP compared to other casino promotional offers. This study conducted a conjoint analysis on patrons’ valuations of FSP compared to other promotional offerings at a casino resort. Moreover, this study investigated the roles inter-casino competition and visitation frequency have on patrons’ perceived valuation of FSP through a hierarchical Bayes model. The results show that competition plays a significant negative role on patrons’ valuation of FSP, while competition held insignificant influence on patrons’ valuation of food and beverage (F&B) comp offers. Additionally, patrons who visited the casino more frequently valued FSP greater, while less active patrons valued F&B comp offers more. Using the study’s results, casinos can increase their margins through increased efficiencies with their promotional offering mix.

Hierarchical Bayes model for random haplotype and family effects in the transmission of fragile-X

2004 ◽  
Vol 26 (4) ◽  
pp. 294-304 ◽  
Author(s):  
R.M. Huggins ◽  
D.Z. Loesch ◽  
G.Q. Qian ◽  
Q.M. Bui ◽  
R.J. Mitchell ◽  
...  
Keyword(s):  
Fragile X ◽  
Hierarchical Bayes ◽  
Family Effects ◽  
Hierarchical Bayes Model ◽  
Bayes Model

scholarly journals Forecasting COVID-19 cases using Machine Learning models

2020 ◽  
Author(s):  
Yuan Tian ◽  
Ishika Luthra ◽  
Xi Zhang
Keyword(s):  
Machine Learning ◽  
Markov Chain Model ◽  
Error Rates ◽  
Hierarchical Bayes ◽  
Public Health Issue ◽  
Chain Model ◽  
Learning Models ◽  
Hierarchical Bayes Model ◽  
Bayes Model ◽  
Machine Learning Models

As of April 26, 2020, more than 2,994,958 cases of COVID-19 infection have been confirmed globally, raising a challenging public health issue. A predictive model of the disease would help allocate medical resources and determine social distancing measures more efficiently. In this paper, we gathered case data from Jan 22, 2020 to April 14 for 6 countries to compare different models' proficiency in COVID-19 cases prediction. We assessed the performance of 3 machine learning models including hidden Markov chain model (HMM), hierarchical Bayes model, and long-short-term-memory model (LSTM) using the root-mean-square error (RMSE). The LSTM model had the consistently smallest prediction error rates for tracking the dynamics of incidents cases in 4 countries. In contrast, hierarchical Bayes model provided the most realistic prediction with the capability of identifying a plateau point in the incidents growth curve.

scholarly journals Estimating Distribution of End Prices Using Hierarchical Bayes Model in B2B Luxury Brand Goods Auctions

2021 ◽  
Vol 36 (5) ◽  
pp. AG21-C_1-12
Author(s):  
Kohei Hatamoto ◽  
Soichiro Yokoyama ◽  
Tomohisa Yamashita ◽  
Hidenori Kawamura
Keyword(s):  
Hierarchical Bayes ◽  
Luxury Brand ◽  
Hierarchical Bayes Model ◽  
Bayes Model
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