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## Details

Genre/Form: | Electronic books |
---|---|

Additional Physical Format: | Print version: Pownuk, Andrew M., 1969- Combining interval, probabilistic, and other types of uncertainty in engineering applications. Cham, Switzerland : Springer, 2018 (OCoLC)1030913081 |

Material Type: | Document, Internet resource |

Document Type: | Internet Resource, Computer File |

All Authors / Contributors: |
Andrew M Pownuk; Vladik Kreinovich |

ISBN: | 9783319910260 3319910264 9783319910277 3319910272 9783030081584 3030081583 |

OCLC Number: | 1034692744 |

Description: | 1 online resource (xi, 202 pages) : illustrations (some color) |

Contents: | Introduction -- How to Get More Accurate Estimates -- How to Speed Up Computations -- Towards a Better Understandability of Uncertainty-Estimating Algorithms -- How General Can We Go: What Is Computable and What Is Not -- Decision Making Under Uncertainty -- Conclusions. |

Series Title: | Studies in computational intelligence, v. 773. |

Responsibility: | Andrew Pownuk, Vladik Kreinovich. |

### Abstract:

How can we solve engineering problems while taking into account data characterized by different types of measurement and estimation uncertainty: interval, probabilistic, fuzzy, etc.? This book provides a theoretical basis for arriving at such solutions, as well as case studies demonstrating how these theoretical ideas can be translated into practical applications in the geosciences, pavement engineering, etc. In all these developments, the authors' objectives were to provide accurate estimates of the resulting uncertainty; to offer solutions that require reasonably short computation times; to offer content that is accessible for engineers; and to be sufficiently general - so that readers can use the book for many different problems. The authors also describe how to make decisions under different types of uncertainty. The book offers a valuable resource for all practical engineers interested in better ways of gauging uncertainty, for students eager to learn and apply the new techniques, and for researchers interested in processing heterogeneous uncertainty.

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