scholarly journals Adaptive Gaussian predictive process models for large spatial datasets

2011 ◽  
Vol 22 (8) ◽  
pp. 997-1007 ◽  
Author(s):  
Rajarshi Guhaniyogi ◽  
Andrew O. Finley ◽  
Sudipto Banerjee ◽  
Alan E. Gelfand
Keyword(s):  
Process Models ◽  
Predictive Process ◽  
Spatial Datasets

scholarly journals Approximate Bayesian inference for large spatial datasets using predictive process models

2012 ◽  
Vol 56 (6) ◽  
pp. 1362-1380 ◽  
Author(s):  
Jo Eidsvik ◽  
Andrew O. Finley ◽  
Sudipto Banerjee ◽  
Håvard Rue
Keyword(s):  
Bayesian Inference ◽  
Process Models ◽  
Predictive Process ◽  
Spatial Datasets ◽  
Approximate Bayesian ◽  
Approximate Bayesian Inference

scholarly journals Business Process Models in the Context of Predictive Process Monitoring

2021 ◽  
Vol 28 (1) ◽  
pp. 39-46
Author(s):  
Florian Spree
Keyword(s):  
Process Monitoring ◽  
Business Process ◽  
Process Model ◽  
Academic Research ◽  
Research Area ◽  
Process Models ◽  
Business Process Model ◽  
Business Process Models ◽  
Wide Range ◽  
Predictive Process

Predictive process monitoring is a subject of growing interest in academic research. As a result, an increased number of papers on this topic have been published. Due to the high complexity in this research area a wide range of different experimental setups and methods have been applied which makes it very difficult to reliably compare research results. This paper's objective is to investigate how business process models and their characteristics are used during experimental setups and how they can contribute to academic research. First, a literature review is conducted to analyze and discuss the awareness of business process models in experimental setups. Secondly, the paper discusses identified research problems and proposes the concept of a web-based business process model metric suite and the idea of ranked metrics. Through a metric suite researchers and practitioners can automatically evaluate business process model characteristics in their future work. Further, a contextualization of metrics by introducing a ranking of characteristics can potentially indicate how the outcome of experimental setups will be. Hence, the paper's work demonstrates the importance of business process models and their characteristics in the context of predictive process monitoring and proposes the concept of a tool approach and ranking to reliably evaluate business process models characteristics.

scholarly journals Gaussian predictive process models for large spatial data sets

2008 ◽  
Vol 70 (4) ◽  
pp. 825-848 ◽  
Author(s):  
Sudipto Banerjee ◽  
Alan E. Gelfand ◽  
Andrew O. Finley ◽  
Huiyan Sang
Keyword(s):  
Spatial Data ◽  
Process Models ◽  
Data Sets ◽  
Predictive Process ◽  
Spatial Data Sets

Multivariate bias adjusted tapered predictive process models

2017 ◽  
Vol 21 ◽  
pp. 42-65 ◽  
Author(s):  
Rajarshi Guhaniyogi
Keyword(s):  
Process Models ◽  
Predictive Process

scholarly journals SGPP: spatial Gaussian predictive process models for neuroimaging data

NeuroImage ◽  
2014 ◽  
Vol 89 ◽  
pp. 70-80 ◽  
Author(s):  
Jung Won Hyun ◽  
Yimei Li ◽  
John H. Gilmore ◽  
Zhaohua Lu ◽  
Martin Styner ◽  
...  
Keyword(s):  
Process Models ◽  
Predictive Process ◽  
Neuroimaging Data

Identifying suboptimalities with factorial model comparison

2018 ◽  
Vol 41 ◽  
Author(s):  
Wei Ji Ma
Keyword(s):  
Experimental Design ◽  
Model Comparison ◽  
Process Models ◽  
Multiple Forms ◽  
Factorial Experimental Design ◽  
Factorial Model ◽  
Multiple Components ◽  
The Many

AbstractGiven the many types of suboptimality in perception, I ask how one should test for multiple forms of suboptimality at the same time – or, more generally, how one should compare process models that can differ in any or all of the multiple components. In analogy to factorial experimental design, I advocate for factorial model comparison.

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