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Full-Text Articles in Other Engineering
Data-Driven Uncertainty Quantification Interpretation With High Density Regions, Matthew Gregor Peterson
Data-Driven Uncertainty Quantification Interpretation With High Density Regions, Matthew Gregor Peterson
Computer Science ETDs
In a time when data is being constantly generated by phones, vehicles, sensor net- works, social media, etc. detecting anomalies with in the data can be very crucial. In cases where we know little prior knowledge about the data, it becomes difficult to extract uncertainty about our results. In this thesis, we will propose a framework in which we can extract uncertainty distributions from data-driven modeling prob- lems. We will show some concrete examples of how to apply framework and provide some insight into what the uncertainty distributions are telling us using High Density Regions (HDRs).
Examples Where The Conjunctive And Dempster’S Rules Are Insensitive, Florentin Smarandache, Jean Dezert, Valeri Kroumov
Examples Where The Conjunctive And Dempster’S Rules Are Insensitive, Florentin Smarandache, Jean Dezert, Valeri Kroumov
Branch Mathematics and Statistics Faculty and Staff Publications
In this paper we present several counter-examples to the Conjunctive rule and to Dempster rule of combinations in information fusion.