## 2020 |

Ristic, Branko; Gilliam, Christopher; Byrne, Marion; Benavoli, Alessio A tutorial on uncertainty modeling for machine reasoning Journal Article Information Fusion, 55 , pp. 30 - 44, 2020, ISSN: 1566-2535. Abstract | Links | BibTeX | Tags: imprecise probability @article{RISTIC2019, title = {A tutorial on uncertainty modeling for machine reasoning}, author = {Branko Ristic and Christopher Gilliam and Marion Byrne and Alessio Benavoli}, url = {http://alessiobenavoli.com/wp-content/uploads/2019/08/Uncertainty_tutorial.pdf}, doi = {https://doi.org/10.1016/j.inffus.2019.08.001}, issn = {1566-2535}, year = {2020}, date = {2020-01-01}, journal = {Information Fusion}, volume = {55}, pages = {30 - 44}, abstract = {Increasingly we rely on machine intelligence for reasoning and decision making under uncertainty. This tutorial reviews the prevalent methods for model-based autonomous decision making based on observations and prior knowledge, primarily in the context of classification. Both observations and the knowledge-base available for reasoning are treated as being uncertain. Accordingly, the central themes of this tutorial are quantitative modeling of uncertainty, the rules required to combine such uncertain information, and the task of decision making under uncertainty. The paper covers the main approaches to uncertain knowledge representation and reasoning, in particular, Bayesian probability theory, possibility theory, reasoning based on belief functions and finally imprecise probability theory. The main feature of the tutorial is that it illustrates various approaches with several testing scenarios, and provides MATLAB solutions for them as a supplementary material for an interested reader.}, keywords = {imprecise probability}, pubstate = {published}, tppubtype = {article} } Increasingly we rely on machine intelligence for reasoning and decision making under uncertainty. This tutorial reviews the prevalent methods for model-based autonomous decision making based on observations and prior knowledge, primarily in the context of classification. Both observations and the knowledge-base available for reasoning are treated as being uncertain. Accordingly, the central themes of this tutorial are quantitative modeling of uncertainty, the rules required to combine such uncertain information, and the task of decision making under uncertainty. The paper covers the main approaches to uncertain knowledge representation and reasoning, in particular, Bayesian probability theory, possibility theory, reasoning based on belief functions and finally imprecise probability theory. The main feature of the tutorial is that it illustrates various approaches with several testing scenarios, and provides MATLAB solutions for them as a supplementary material for an interested reader. |

# Publications

## 2020 |

A tutorial on uncertainty modeling for machine reasoning Journal Article Information Fusion, 55 , pp. 30 - 44, 2020, ISSN: 1566-2535. |