9 edition of **Interval/probabilistic uncertainty and non-classical logics** found in the catalog.

- 370 Want to read
- 16 Currently reading

Published
**2008**
by Springer in Berlin
.

Written in English

- Uncertainty (Information theory) -- Congresses,
- Nonclassical mathematical logic -- Congresses

**Edition Notes**

Statement | Van-Nam Huynh ... [et al.], (eds.). |

Genre | Congresses. |

Series | Advances in soft computing -- 46 |

Contributions | Huynh, Van-Nam. |

Classifications | |
---|---|

LC Classifications | Q375 .I57 2008 |

The Physical Object | |

Pagination | xviii, 375 p. : |

Number of Pages | 375 |

ID Numbers | |

Open Library | OL17072302M |

ISBN 10 | 354077663X |

ISBN 10 | 9783540776635 |

LC Control Number | 2007942801 |

In , a BMJ-edition book straightforwardly pointed-out the importance of providing effect measures with confidence intervals (CI) when reporting the results of clinical/epidemiological research, and not only the results of statistical r, medical doctors commonly seem to be more aware of formal statistical testing and more fascinated with statistical significance than they are. Fourteen chapters are organized into three parts: mathematical logic and foundations (Chapters ), general topology (Chapters ), and measure and probability theory (Chapters ). Chapter 1 deals with non-classical logics and their syntactic and semantic foundations.

Imprecise probability generalizes probability theory to allow for partial probability specifications, and is applicable when information is scarce, vague, or conflicting, in which case a unique probability distribution may be hard to identify. Thereby, the theory aims to represent the available knowledge more accurately. Imprecision is useful for dealing with expert elicitation, because. Complexity is well handled by first-order logic, and uncertainty by probabilistic graphical models. What has been sorely missing is a seamless combination of the two. Markov logic networks (MLNs) provide this by attaching weights to logical formulas and treating them .

Probabilistic Reasoning and Reasoning with Probabilities. Studies in Logic (pp. ). College Publications Jonczy, Jacez; Haenni, Rolf (). Network Reliability Evaluation with Propositional Directed Acyclic Graphs. Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster.

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This book contains proceedings of the first international workshop that brought together researchers working on interval and probabilistic uncertainty and on non-classical logics. We hope that this workshop will lead to a boost in the much-needed collaboration between the uncertainty analysis and non-classical logic communities, and thus, to better processing of uncertainty.

An Algebraic Approach to Substructural Logics – An Overview Hiroakira Ono. On Modeling of Uncertainty Measures and Observed Processes Hung T. Nguyen. Statistics under Interval Uncertainty and Imprecise Probability.

Fast Algorithms for Computing Statistics under Interval Uncertainty: An Overview Vladik Kreinovich, Gang Xiang. Edition: 1. This book contains the proceedings of the first International Workshop on Interval/Probabilistic Uncertainty and Non Classical Logics, Ishikawa, Japan, March Interval / Probabilistic Uncertainty and Non-Classical Logics Book January with 24 Reads How we measure 'reads' A 'read' is counted each time someone views a publication summary (such.

This book contains the proceedings of the first International Workshop on Interval/Probabilistic Uncertainty and Non Classical Logics, Ishikawa, Japan, MarchThe workshop brought together researchers working on interval and probabilistic uncertainty and on non-classical logics. book explains how to generate an adequate description of uncertainty, how to justify.

semiheuristic algorithms for processing uncertainty, and how to make these algorithms. more computationally efficient. It explains in what sense the existing approach to.

uncertainty as a combination of random and systematic components is only an. Interval / Probabilistic Uncertainty and Non-Classical Logics. Interval / Probabilistic Uncertainty and Non-Classical Logics pp Huynh VN., Nakamori Y., Ono H., Lawry J., Kreinovich V., Nguyen H.T.

(eds) Interval / Probabilistic Uncertainty and Non-Classical Logics. Advances in Soft Computing, vol Springer, Berlin, Heidelberg. Books of Interest to Interval Researchers. Interval/Probabilistic Uncertainty and Non-Classical Logics, by V.-N.

Huynh, Y. Nakamori, H. Ono, J. Lawry, V. Kreinovich, and H. Nguyen, (Eds.), Springer Verlag, Berlin-Heidelberg,ISBN GO Interval / Probabilistic Uncertainty and Non-classical Logics Author: Hiroakira Ono, Hung T. Nguyen, Jonathan Lawry, Van-Nam Huynh, Vladik Kreinovich, Yoshiteru Nakamori Type: eBook Language: English Released: Publisher: Springer Page Count: Format: pdf ISBN X ISBN Interval / Probabilistic Uncertainty and Non-classical Logics by Van-Nam Huynh and Publisher Springer.

Save up to 80% by choosing the eTextbook option for ISBN:The print version of this textbook is ISBN:from book Interval / Probabilistic Uncertainty and Non-Classical Logics (pp) Possible Semantics for a Common Framework of Probabilistic Logics Chapter January with 35 Reads. Kreinovich, and Hung T.

Nguyen (eds.), Interval/Probabilistic Uncertainty and Non-Classical Logics, Springer-Verlag, Berlin-Heidelberg-New York, L. Reznik and V. Kreinovich (eds.), Soft Computing in Measurements and Infor. This paper develops and illustrates a probabilistic approach for uncertainty representation and propagation in system analysis, when the information on the uncertain input variables and/or their distribution parameters may be available as either probability distributions or simply intervals.

Bi, Yaxin; Shen, Xuhui ; Wu, Shengli./ Uncertainty Reasoning in Rough Knowledge al / Probabilistic Uncertainty and Non-Classical Logics Advances in Soft Computing. Vkladik Kreinovich: free download.

Ebooks library. On-line books store on Z-Library | B–OK. Download books for free. Find books. Non-classical Logics: from Foundations to Applications, AprilPisa. International Workshop on Interval/Probabilistic Uncertainty and Non-Classical Logics, Japan Advanced Institute of Science and Technology (JAIST), MarchIshikawa, Japan.

Winter School in Logic, India Institute of Technology, JanuaryKanpur. BibTeX @INPROCEEDINGS{Haenni08possiblesemantics, author = {Rolf Haenni and Jan-willem Romeijn and Gregory Wheeler and Jon Williamson}, title = {Possible semantics for a common framework of probabilistic logics}, booktitle = {UncLog’08, International Workshop on Interval/Probabilistic Uncertainty and Non-Classical Logics, Advances in Soft Computing}, year = {}}.

We present a propositional logic to reason about the uncertainty of events, where the uncertainty is modeled by a set of probability measures assigning an interval of probability to each event.

We give a sound and complete axiomatization for the logic, and show that the satisfiability problem is NP-complete, no harder than satisfiability for.

Author: L. Kolev. Publisher: World Scientific ISBN: Page: View: : Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion (Studies in Systems, Decision and Control) (): Servin, Christian, Kreinovich, Vladik: Books.

Possible Semantics for a Common Framework of Probabilistic Logics. Gregory Wheeler, Jon Williamson, Jan-Willem Romeijn & Rolf Haenni - - In V. N. Huynh (ed.), International Workshop on Interval Probabilistic Uncertainty and Non-Classical Logics.

Springer.In this paper intervals are used to model uncertainty in parameter estimation problemsfor instance, some noise associated with measured data. Probabilistic Constraints for Inverse Problems.

Interval / Probabilistic Uncertainty and Non-Classical Logics, Transition probability from AIDS to death. The transition probabilities from state A. Since the log of a relative risk follows a lognormal distribution, relative risk follows a lognormal distribution whose mean is rr and standard deviation on the log scale can be deduced from the relative risk confidence interval.