4 edition of **Entropy in relation to incomplete knowledge** found in the catalog.

- 38 Want to read
- 9 Currently reading

Published
**1985** by Cambridge University Press in Cambridge [Cambridgeshire], New York .

Written in English

- Entropy.,
- Entropy (Information theory),
- Physics -- Philosophy.,
- Knowledge, Theory of.

**Edition Notes**

Statement | K.G. Denbigh, J.S. Denbigh. |

Contributions | Denbigh, J. S. 1938- |

Classifications | |
---|---|

LC Classifications | QC318.E57 D46 1985 |

The Physical Object | |

Pagination | vii, 164 p. : |

Number of Pages | 164 |

ID Numbers | |

Open Library | OL2860331M |

ISBN 10 | 0521256771 |

LC Control Number | 84023108 |

5 The fact that this equation defines only a difference of entropy is of no relevance in this note where the issue is whether entropy changes are related to changes -of 'orderliness' or of 'organization'. 6 For a discussion on the relationships between thermodynamic entropy and some of the statistical entropies see, for example, Penrose []. C. G. Chakrabarti, I. Chakrabarty 2 2. THERMODYNAMIC PROBABILITY AND BOLTZMANN ENTROPY Boltzmann entropy is defined by [1] S = k lnW () where k is the thermodynamic unit of the measurement of the entropy and is the Boltzmann constant, W called the thermodynamic probability or statistical weight is the total number of microscopic states or complexions compatible with.

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This book is about an important issue which has arisen within two of the branches of physical science - namely thermodynamics and statistical mechanics - where the notion of entropy plays an essential role.

A number of scientists and information theorists have maintained that entropy is a subjective concept and is a measure of human by: Whilst the present volume is not a treatise on thermodynamics or statistical mechanics, all relevant steps in the building up of these disciplines are carefully scrutinised and it is concluded that the charge of subjectivity cannot be upheld.

The widely adopted view that entropy is a measure of disorder, /5(2). Entropy in relation to incomplete knowledge. [Kenneth George Denbigh; J S Denbigh] -- Through the use of thermodynamics and statistical mechanics the author discusses the idea of entropy as subjective, and the philosophical issues with this theory.

This option allows users to search by Publication, Volume and Page Selecting this option will search the current publication in context. Selecting this option will search all publications across the Scitation platform Selecting this option will search all publications for the Publisher/Society in contextCited by: Entropy in Relation to Incomplete Knowledge K.

Denbigh, J. Denbigh This book is about an important issue which has arisen within two of the branches of physical science - namely thermodynamics and statistical mechanics - where the notion of entropy plays an essential role. Entropy in relation to incomplete knowledge K.

DENBIGH Honorary Research Fellow, Chelsea College University of London J. DENBIGH Mathematician, St George's Hospital Medical School, London The University has primed and published continuously since CAMBRIDGE UNIVERSITY PRESS Cambridge London New York New Rochelle Melbourne Sydney.

Entropy in Relation to Incomplete Knowledge 内容简介 This book is about an important issue which has arisen within two of the branches of physical science - namely thermodynamics and statistical mechanics - where the notion of entropy plays an essential : Kenneth Denbigh.

Denbigh, K.G. and Denbigh, J.S. () Entropy in relation to incomplete knowledge. Cambridge University Press, Cambridge.

KG Denbigh and JS Denbigh, Entropy in Relation to Incomplete Knowledge Reviewed By. Lawrence Sklar - - Philosophy in Review 7 (2) Entropy in Relation to Incomplete by: Incomplete knowledge implies that obtained results may not be unique.

That is, results may be ambiguous. The method of maximum entropy focuses on the consequences of improper assignments of unknown statistical information. Covariance intersection Related Book. : Jan Peter Hessling. Information entropy, rough entropy and knowledge granulation in incomplete information systems December International Journal of General Systems 35(6) Read "Entropy in relation to incomplete knowledge, The American Journal of Physics" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.

As an application of knowledge granulation, we introduce definition of rough entropy of rough sets in ordered information systems. By an example, it is shown that the rough entropy of rough sets is more accurate than classical rough degree to measure the roughness of rough sets.

Though the book is technically detailed, it does not assume too much knowledge on the part of the reader; a quick crash course on the mathematical machinery of quantum mechanics is included in the appendix, for example.5/5.

@MISC{Objectivity_contentsi, author = {I. Objectivity}, title = {CONTENTS I Entropy in Relation to Incomplete Knowledge}, year = {}} Share. OpenURL. Abstract. I.2 Entropy as a secondary quality. I.3 The significance of the failure of classical determinism.

Keyphrases. In addition, the notion of entropy was not useful when dealing with processes with contin- uous alphabets since it is generally in nite in such cases. A generalization of the idea of entropy called discrimination was developed by Kullback (see, e.g., Kullback [93]) and was further studied by the Soviet Size: 1MB.

Abstract. Based on the intuitionistic knowledge content characteristic of information gain, the concepts of combination entropy CE(A) and combination granulation CG(A) in incomplete information system are introduced, their some properties are rmore, the relationship between combination entropy and combination granulation is by: Not Available adshelp[at] The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Agreement NNX16AC86ACited by: cond-mat/; cond-mat/], suggesting Tsallis entropy to be not a fundamental concept but a derived one, stemming from an incomplete knowledge of the system, not taking properly into account its interaction with the environment.

This. Abstract. This paper is intended to be a contribution to a debate about the status of thermodynamic entropy. The question at issue is whether entropy is a fully objective quantity, independent of all observers, or whether, on the contrary, it is a subjective : W. Day. Review of K.G.

& J.S. Denbigh, Entropy in Relation to Incomplete Knowledge, Cambridge University Press,and H.D. Zeh, The Physical Basis of the Direction of Time, Springer-Verlag, Berlin, ; British Journal for the Philosophy of Science 42() — irreversibility and incomplete knowledge.

In the strictly mathematical sense entropy is related to the asymptotics of probabilities or it is a kind of asymptotic behaviour. That depends on what kind of entropy you're interested in: there are more entropy variations than you can shake a stick at. For an overview of the most commonly seen "entropies," see What is the easiest definition of "entropy".

and follow the link. " 1 This note is based on a paper given to a B.S.P.S. meeting at Sussex University in Septemberand on the book Entropy in Relation to Incomplete Knowledge by K.

Denbigh and J. Denbigh, Cambridge University Press ().Cited by: O.J. T apiero / The relationship between risk and incomplete states uncertainty: a Tsallis entr opy perspective 1 0 1 2 1 4 1 6 1 8 Number of assets in a portfolio. CONTENTS I Entropy in Relation to Incomplete Knowledge. By I. Objectivity.

Abstract. I.2 Entropy as a secondary quality. I.3 The significance of the failure of classical determinis Year: OAI identifier: oai: Author: I. Objectivity. The information entropy, often just entropy, is a basic quantity in information theory associated to any random variable, which can be interpreted as the average level of "information", "surprise", or "uncertainty" inherent in the variable's possible concept of information entropy was introduced by Claude Shannon in his paper "A Mathematical Theory of Communication".

In this paper, a novel feature selection method based on the neighborhood rough sets using Lebesgue and entropy measures in incomplete neighborhood decision systems is proposed, and the method has the capacity to handle mixed and incomplete datasets; further, it can simultaneously maintain the original classification by: 2.

Feature selection in incomplete decision table has gained considerable attention in recently. However many feature selection methods are mainly designed for incomplete data with categorical features. In this paper, we introduce an extended rough set model, which is based on neighborhood-tolerance relation and is applicable to incomplete data Cited by: Proposition 7, Proposition 8 establish the relationships among the rough entropy, the conditional entropy and the mutual information in incomplete information systems.

Moreover, these relationships are helpful to understand the essence of the knowledge content and the. The entropy of an LCA model as the number of classes goes to the number of unique records has not, to our knowledge, been examined.

To that end, we present a proof to describe that behavior. Using the work of Fruhwirth-Schnatter [ 11 ] and Dias and Vermunt [ 18 ], altered to consider only unique records, we begin with p (X = x i):Cited by: 3.

The information entropy, rough entropy and knowledge granulation in rough set theory. M.W. and Zhang, W.X., Dominance relation and rules in an incomplete ordered information system. S.L., Sun, J.X. and Li, Z.Y., Attribute reduction algorithm based on conditional entropy under incomplete information system.

Journal of National University Author: SunLin, XuJiucheng, TianYun. Yao defined a granulation measure from the viewpoint of granulation [39]. Dai et al. proposed a new type of conditional entropy based on tolerance relation for an incomplete decision system [6] [7.

An idea that can be found in the literature is that entropy is a property of a statistical ensemble of (many identically prepared) systems, and cannot be associated with a single system. It originates from a particular interpretation of statistical mechanics, an interpretation full of foundational difficulties.

To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance, cover and electronic interference and it is related to the state of a target in air combat.

Predicting the target intention is helpful to know the target actions Author: Tongle Zhou, Mou Chen, Yuhui Wang, Jianliang He, Chenguang Yang.

The principle of maximum entropy states that the probability distribution which best represents the current state of knowledge is the one with largest entropy, in the context of precisely stated prior data (such as a proposition that expresses testable information). Another way of stating this: Take precisely stated prior data or testable information about a probability distribution function.

Reduction algorithm based on finding the maximum mutual information in incomplete information systems. Hu Feng Knowledge reduction method of incomplete information system based on decision entropy[J] Dai, Xu Q., Wang W. and Tian H. Conditional entropy for incomplete decision systems and its application in data mining[J] Author: Weibing Feng, Manting Zhang.

Shannon entropy has been related by physicist Léon Brillouin to a concept sometimes calledBrillouin derived a general equation stating that the changing of an information bit value requires at least kT ln(2) energy. This is the same energy as the work Leo Szilard's engine produces in the idealistic case, which in turn equals to the same quantity found by Landauer.

S stands for entropy and belongs to the macro world described by thermodynamics. Ωbis the number of micro states of a macroscopic system4. kB is the Boltzmann constant 5 that establishes correspondence of the statistical entropy of Boltzmann to the thermodynamic entropy of Clausius 6.

Boltzmann-Gibbs-Shannon EntropyFile Size: 1MB. Rough set theory is emerging as a powerful tool for reasoning about data, knowledge reduction is one of the important topics in the research on rough set theory. It has been proven that finding the minimal reduct of an information system is a NP-hard problem, so is finding the minimal reduct of an incomplete information system.

Knowledge reduction is an important issue in data mining. This paper focuses on the problem of knowledge reduction in incomplete decision tables.

Based on a concept of incomplete conditional entropy, a new reduct definition is presented for incomplete decision tables .theories, albeit with second-hand and incomplete knowledge of some of them -and at the risk of making fools of ourselves.

So much for my apology. The difficulties of language are not negligible. One's native speech is a closely fitting garment, and one never feels quite at ease when it is not immediately available and has to be replaced by another.We discuss the incomplete knowledge and its relation to the information theory in the next section.

The third section is devoted to introducing the escort probability and derivation of Tsallis entropy from axioms of the incomplete information theory. Finally we summarize Cited by: