8 citations · 21 across the 8 of their papers we have counts for
8 papers
Redeeming Data Science by Decision Modelling
John Mark Agosta, Robert Horton
With the explosion of applications of Data Science, the field is has come loose from its foundations. This article argues for a new program of applied research in areas familiar to…
Hierarchical Bayesian Regression for Multi-Location Sales Transaction Forecasting
John Mark Agosta, Mario Inchiosa
The features in many prediction models naturally take the form of a hierarchy. The lower levels represent individuals or events. These units group naturally into locations and inte…
Runtime Variation in Big Data Analytics
Yiwen Zhu, Rathijit Sen, Robert Horton +2
The dynamic nature of resource allocation and runtime conditions on Cloud can result in high variability in a job's runtime across multiple iterations, leading to a poor experience…
The structure of Bayes nets for vision recognition
John Mark Agosta
This paper is part of a study whose goal is to show the effciency of using Bayes networks to carry out model based vision calculations. [Binford et al. 1987] Recognition proceeds b…
Model-based Influence Diagrams for Machine Vision
Tod S. Levitt, John Mark Agosta, Thomas O. Binford
We show an approach to automated control of machine vision systems based on incremental creation and evaluation of a particular family of influence diagrams that represent hypothes…
"Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models
John Mark Agosta
This paper examines the interdependence generated between two parent nodes with a common instantiated child node, such as two hypotheses sharing common evidence. The relation so ge…