Dominik Hose

Possibilistic Reasoning with Imprecise Probabilities: Statistical Inference and Dynamic Filtering


Front	Back

ISBN:

978-3-8440-8721-5

Series:

Schriften aus dem Institut für Technische und Numerische Mechanik der Universität Stuttgart
Herausgeber: Prof. Dr.-Ing. Peter Eberhard
Stuttgart

Volume:

2022,74

Keywords:

Uncertainty Quantification; Statistical Inference; Possibility Theory; Filtering; Imprecise Probabilities

Type of publication:

Thesis

Language:

English

Pages:

236 pages

Figures:

87 figures

Weight:

350 g

Format:

21 x 14,8 cm

Bindung:

Paperback

Price:

49,80 € / 62,30 SFr

Published:

August 2022

Buy:

Download:

Available PDF-Files for this title:

You need the Adobe Reader, to open the files. Here you get help and information, for the download.

These files are not printable.


	Document		Document
	Type		PDF
	Costs		37,35 EUR
	Action		Purchase in obligation and display of file - 4,3 MB (4527202 Byte)
	Action		Purchase in obligation and download of file - 4,3 MB (4527202 Byte)


	Document		Table of contents
	Type		PDF
	Costs		free
	Action		Display of file - 167 kB (170871 Byte)
	Action		Download of file - 167 kB (170871 Byte)

User settings for registered users

You can change your address here or download your paid documents again.

User:	Not logged in.
Actions:	Login / Register Forgotten your password?

Recommendation:

You want to recommend this title?

Review copy:

Here you can order a review copy.

Link:

You want to link this page? Click here.

Export citations:

Text
BibTex
RIS

Abstract:

Two effects can often be observed when checking numerical simulation models against measured data from the described physical systems. On the one hand, a precise specification of all parameters in these models is often impossible for various reasons---sometimes, they do not even describe any physical quantities that could be measured.
Moreover, many physical processes contain a natural degree of variability which cannot be described by deterministic models, not even by highly accurate ones. If actual measurements are to be used to calibrate the model, i.e. its unknown parameters, it is evident that noisy data will never allow an exact inference. This inexactness, in turn, implies that arbitrarily accurate statements, especially predictions, cannot be made about the behavior of the physical system. Such observations provide a direct motivation for uncertainty quantification, which aims to describe, quantify, and compute these very uncertainties.

This thesis considers possibilistic methods for statistical inference with imprecise probabilities. In this sense, it belongs to a young subfield of statistics and uncertainty quantification which has its origins in the Dempster-Shafer theory of evidence and the theory of fuzzy sets by Zadeh.
More precisely, it makes a contribution to the applied theory of imprecise probabilities by linking theoretical and numerical insights with practical applications in engineering and showing how possibilistic methods can be used profitably.