Last edited by Mooguktilar

Sunday, August 2, 2020 | History

6 edition of **Probabilistic Logic in a Coherent Setting (Trends in Logic)** found in the catalog.

- 111 Want to read
- 4 Currently reading

Published
**October 31, 2002**
by Springer
.

Written in English

- Mathematical logic,
- Mathematical theory of computation,
- Mathematics,
- Mathematical And Symbolic Logic,
- Probabilities,
- Science/Mathematics,
- General,
- Logic,
- Philosophy / Logic,
- Probability & Statistics - General,
- Logic, Symbolic and mathematic,
- Logic, Symbolic and mathematical

The Physical Object | |
---|---|

Format | Hardcover |

Number of Pages | 296 |

ID Numbers | |

Open Library | OL8370305M |

ISBN 10 | 1402009178 |

ISBN 10 | 9781402009174 |

probabilistic logic and entailment in probabilistic logic un-der coherence. Roughly speaking, the main difference be-tween model-theoreticprobabilistic entailment and proba-bilistic entailment under coherence is that the former real-izes an inheritance of logical knowledge, while the latter does not. Intuitively, the new formalisms now add a strat-. The Independent Choice Logic began in the early 90's as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability.

networks with logic programming; and the third setting, learning from proofs, incorporated in stochastic logic programs [12,30,4], upgrades stochastic context free grammars to logic programs. The sketched settings (and their instances presented) are by no means the only possible settings for probabilistic induc-tive logic programming. Probabilistic logic under coherence, model-theoretic probabilistic logic, and default reasoning in System P Veronica Biazzo, Angelo Gilio, Thomas Lukasiewicz and Giuseppe Sanfilippo 13 April | Journal of Applied Non-Classical Logics, Vol. 12, No. 2.

Quantum probability is a subtle blend of quantum mechanics and classical probability theory. Its important ideas can be traced to the pioneering work of Richard Feynman in his path integral recently have the concept and ideas of quantum probability been presented in a rigorous axiomatic framework, and this book provides a coherent and . We take coherence based probability logic as the basic reference theory to model human deductive reasoning. The conditional and probabilistic argument forms are explored. We give a brief overview of recent developments of combining logic and probability in psychology. A study on conditional inferences illustrates our approach.

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Probabilistic Logic in a Coherent Setting (Trends in Logic Book 15) - Kindle edition by Coletti, Giulianella, Scozzafava, R. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Probabilistic Logic in a Coherent Setting (Trends in Logic Book 15).Author: Giulianella Coletti, R.

Scozzafava. Probabilistic Logic in a Coherent Setting (Trends in Logic (15)) [Coletti, Giulianella, Scozzafava, R.] on *FREE* shipping on qualifying offers.

Probabilistic Logic in a Coherent Setting (Trends in Logic (15))Cited by: This book is a real asset for people who work in the ﬁeld of op-erator semigroups, or want to.

It is a beauty, not only because it. Probabilistic Logic in a Coherent Setting. Get this from a library. Probabilistic Logic in a Coherent Setting. [Giulianella Coletii; Romano Scozzafava] -- The approach to probability theory followed in this book (which differs radically from the usual one, based on a measure-theoretic framework) characterizes probability as a linear operator rather.

Coherent Extensions of Probability Assessments Random Quantities Probability Meaning and Assessment: a Reconciliation To Be or not To Be Compositional. Conditional Events Coherent Conditional Probability Zero-Layers Coherent Extensions of Conditional Probability Exploiting Zero Probabilities The approach to probability theory followed in this book (which differs radically from the usual one, based on a measure-theoretic framework) characterizes probability as a linear operator rather than as a measure, and is based on the concept of coherence, which can be framed in the most general view of conditional probability.

The approach to probability theory followed in this book (which differs radically from the usual one, based on a measure-theoretic framework) characterizes probability as a linear operator rather than as a measure, and is based on the concept of coherence, which can be framed in the most general view of conditional probability.

It is a flexible' and unifying tool suited for. A coherent conditional probability assessmen t { P (E Ï | x) } x œ C X measures the degree of belief of You in E Ï,w h e n X assumes the di ﬀ erent values of its. Coletti G., Scozzafava R.

Probabilistic Logic in a Coherent Setting. The approach adopted in this book is based on the concept of coherence, that can be framed in the most general view of conditional probability (as proposed by Bruno de Finetti), and it is apt to avoid the usual criticisms, making also a clear-cut distinction between the.

Cite this chapter as: Coletti G., Scozzafava R. () Stochastic Independence in a Coherent Setting. In: Probabilistic Logic in a Coherent Setting. Probabilistic Fuzzy Reasoning in a Coherent Setting.

Authors. Giulianella Coletti AU - Davide Petturiti AU - Barbara Vantaggi PY - /08 DA - /08 TI - Probabilistic Fuzzy Reasoning in a Coherent Setting BT - 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT) PB - Atlantis Press SN - UR - https Author: Giulianella Coletti, Davide Petturiti, Barbara Vantaggi.

The logic FCP(ŁΠ), which is built up over the many-valued logic ŁΠ (a logic which combines the well-known Łukasiewicz and Product fuzzy logics), was shown to be complete for modal theories with respect to the class of probabilistic Kripke structures induced by coherent conditional probabilities.

Indeed, checking coherence of a (generalized. Probabilistic logic is a method of dealing with partially specified probabilistic models, where the specification is in the form of probability assignments to a select set of logical sentences. Any such assignment defines a range of permissible complete models, and by describing the boundaries of this range one can deduce bounds on the.

Corollary 1 Partial independence from below. If the set of atoms in which the indicator of the conclusion is 0 is logically independent, then p ′ = Corollary 2 Partial independence from above.

If the set of atoms in which the indicator of the conclusion is 1 is logically independent, then p ″ = One of the best known principles in probability logic is.

The aim of a probabilistic logic (also probability logic and probabilistic reasoning) is to combine the capacity of probability theory to handle uncertainty with the capacity of deductive logic to exploit structure of formal result is a richer and more expressive formalism with a broad range of possible application areas.

Probabilistic logics attempt to find a natural. It is purely a question of studying it and saying whether it is coherent or not; i.e., whether it is free of, or affected by, intrinsic contradictions.

In the same way, in the logic of certainty one ascertains the correctness of the deductions but not the accuracy of the factual data assumed as premises. Bruno de Finetti Theory of Probability I. Probabilistic Logic under Coherence, Model-Theoretic Probabilistic Logic, and Default Reasoning is g-coherent iff there exists a coherent precise probability assessment 9K~ }`~ on b such that `_ X c MN` c for all c&M b.

Let 9K_ a` be a g-coherentimprecise probability assessment on a set of conditional events b. Romano Scozzafava (born Novem ) is an Italian mathematician known for his contributions to subjective probability along the lines of Bruno de Finetti, based on the concept of taught Probability Calculus at the Engineering Faculty of the Sapienza University of Rome from to his retirement (at the end of ).

Scozzafava has conducted Alma mater: Sapienza University of Rome. Probabilistic Logic Learning Probability Logic Learning Figure 1: Probabilistic Logic Learning as the intersection of Probability, Logic, and Learning. ing, diagnostic and troubleshooting, information retrieval, software debugging, data mining and user modelling.

The term logic in the present overview refers to ﬁrst order. Jaynes died Ap Before his death he asked me to nish and publish his book on probability theory. I struggled with this for some time, because there is no doubt in my mind that Jaynes wanted this book nished.

Unfortunately, most of the later Chapters, Jaynes’ intended. Probabilistic models provide a sound and coherent foundation for dealing with the noise and uncertainty encountered in most real- world domains. Bayesian networks are a language for representing complex probabilistic models in a compact and natural way.Probabilistic logic in a coherent setting.

By Giulianella Coletii and Romano Scozzafava. Cite. BibTex; Full citation; Topics: Mathematical Physics and .Translated in Jeffrey, R.

C. (ed.), Studies in Inductive Logic and Probability II, pages –, University of California Press, Los Angeles, de Finetti, B. La probabilita e la statistica nei raporti con l'induzione, secondo i dwersi punti di vista.