### Abstract

In the literature, two main views of Dempster-Shafer (DS) theory are espoused: DS theory as evidence (as described in Shafer's seminal book) and DS theory as a generalization of probability. These two views are not always consistent. In this paper, we employ the generalized probability view of DS theory to arrive at results that allow one to perform Bayesian inference within the DS theoretic (DST) framework. The importance of this generalization is its capability of handling a wider variety of data imperfections, a feature inherited from the DST framework. In the process of developing these results akin to Bayesian inference, we also arrive at an evidence combination strategy which is consistent with the generalized probability view of DS theory, a feature lacking in the popular Dempster's combination rule (DCR). Finally, using the data from a political science survey, we demonstrate the application of our results on an experiment which attempts to gauge the hidden attitude of an individual from his/her observed behavior.

Original language | English (US) |
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Title of host publication | FUSION 2016 - 19th International Conference on Information Fusion, Proceedings |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 798-804 |

Number of pages | 7 |

ISBN (Electronic) | 9780996452748 |

State | Published - Aug 1 2016 |

Event | 19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, Germany Duration: Jul 5 2016 → Jul 8 2016 |

### Other

Other | 19th International Conference on Information Fusion, FUSION 2016 |
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Country | Germany |

City | Heidelberg |

Period | 7/5/16 → 7/8/16 |

### Fingerprint

### ASJC Scopus subject areas

- Statistics, Probability and Uncertainty
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Signal Processing

### Cite this

*FUSION 2016 - 19th International Conference on Information Fusion, Proceedings*(pp. 798-804). [7527968] Institute of Electrical and Electronics Engineers Inc..

**A generalization of Bayesian inference in the Dempster-Shafer belief theoretic framework.** / Heendeni, J. N.; Premaratne, Kamal; Murthi, Manohar; Uscinski, Joseph; Scheutz, M.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*FUSION 2016 - 19th International Conference on Information Fusion, Proceedings.*, 7527968, Institute of Electrical and Electronics Engineers Inc., pp. 798-804, 19th International Conference on Information Fusion, FUSION 2016, Heidelberg, Germany, 7/5/16.

}

TY - GEN

T1 - A generalization of Bayesian inference in the Dempster-Shafer belief theoretic framework

AU - Heendeni, J. N.

AU - Premaratne, Kamal

AU - Murthi, Manohar

AU - Uscinski, Joseph

AU - Scheutz, M.

PY - 2016/8/1

Y1 - 2016/8/1

N2 - In the literature, two main views of Dempster-Shafer (DS) theory are espoused: DS theory as evidence (as described in Shafer's seminal book) and DS theory as a generalization of probability. These two views are not always consistent. In this paper, we employ the generalized probability view of DS theory to arrive at results that allow one to perform Bayesian inference within the DS theoretic (DST) framework. The importance of this generalization is its capability of handling a wider variety of data imperfections, a feature inherited from the DST framework. In the process of developing these results akin to Bayesian inference, we also arrive at an evidence combination strategy which is consistent with the generalized probability view of DS theory, a feature lacking in the popular Dempster's combination rule (DCR). Finally, using the data from a political science survey, we demonstrate the application of our results on an experiment which attempts to gauge the hidden attitude of an individual from his/her observed behavior.

AB - In the literature, two main views of Dempster-Shafer (DS) theory are espoused: DS theory as evidence (as described in Shafer's seminal book) and DS theory as a generalization of probability. These two views are not always consistent. In this paper, we employ the generalized probability view of DS theory to arrive at results that allow one to perform Bayesian inference within the DS theoretic (DST) framework. The importance of this generalization is its capability of handling a wider variety of data imperfections, a feature inherited from the DST framework. In the process of developing these results akin to Bayesian inference, we also arrive at an evidence combination strategy which is consistent with the generalized probability view of DS theory, a feature lacking in the popular Dempster's combination rule (DCR). Finally, using the data from a political science survey, we demonstrate the application of our results on an experiment which attempts to gauge the hidden attitude of an individual from his/her observed behavior.

UR - http://www.scopus.com/inward/record.url?scp=84992021813&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84992021813&partnerID=8YFLogxK

M3 - Conference contribution

SP - 798

EP - 804

BT - FUSION 2016 - 19th International Conference on Information Fusion, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -