Cause-Effect Structures
Cause-Effect Structures

Author: Ludwik Czaja

Publisher: Springer

Published: 2019-05-27

Total Pages: 153

ISBN-13: 3030204618

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This book presents a new algebraic system whose interpretation coincides with the behaviour of Petri nets, enhanced with an inhibitory mechanism and four time models. Its goal is to provide a formal means of modelling dynamic tasks, and of testing and verifying properties, in contexts characterised by the parallel execution of actions. However, the task description differs from that of Petri nets. The algebra is a quasi-semiring, “quasi” because of its somewhat restricted distributivity axiom. Expressions of this algebra, the cause–effect structures, have a graphic presentation as nets, but with one kind of named nodes, each annotated by two expressions that specify the type of signal reception from predecessors and transmission to successors. Many structural and behavioural properties are stated with proofs, and illustrative sample tasks are included. The book is intended for all those interested or involved in parallel and distributed computing – students, researchers and practitioners alike.

Oregon Writes
Oregon Writes

Author: Jenn Kepka

Publisher:

Published: 2016

Total Pages:

ISBN-13:

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This textbook guides students through rhetorical and assignment analysis, the writing process, researching, citing, rhetorical modes, and critical reading. Using accessible but rigorous readings by professionals throughout the college composition field, the Oregon Writes Writing Textbook aligns directly to the statewide writing outcomes for English Composition courses in Oregon. Created through a grant from Open Oregon in 2015-16, this book collects previously published articles, essays, and chapters released under Creative Commons licenses into one free textbook available for online access or print-on-demand.

Computing in Cause-Effect Structures
Computing in Cause-Effect Structures

Author: Ludwik Czaja

Publisher: Springer Nature

Published: 2021-11-27

Total Pages: 180

ISBN-13: 3030888134

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This book focuses on numerous examples of tasks represented by c-e structure. Cause–effect (c-e) structures are dynamic objects devised for algebraic and graphic description of realistic tasks. They constitute a formal system providing means to specify or implement (depending on degree of description generality) the tasks. They can be transformed, thus come under simplification, in accordance with rules-axioms of their algebra. Also, their properties can be inferred from the axioms. One objective of this book is presentation, by many realistic examples, of computing capability of c-e structures, without entering into mathematical details of their algebra. In particular, how computing with natural numbers and in propositional calculus can be performed by c-e structures and how to specify behavior of data structures. But also demonstration of many other tasks taken from the area of parallel processing, specified as c-e structures. Another objective is modelling or simulation by means of c-e structures, of other descriptive systems, devised for tasks from various fields. Also without formalizing by usage of functions between the systems. This concerns formalisms such as reaction systems, rough sets, Petri nets and CSP-like languages. Also on such, where temporal interdependence between actions matters. The presentation of examples is prevalently graphic, in the form of peculiar nets, but accompanied by their algebraic and set-theoretic expressions. A fairly complete exposition of concepts and properties of the algebra of cause-effect structures is in the previous book appeared in the Lecture Notes in Networks and Systems series. But basic notions of c-e structures are here provided for understanding the examples.

Cause Effect Pairs in Machine Learning
Cause Effect Pairs in Machine Learning

Author: Isabelle Guyon

Publisher: Springer Nature

Published: 2019-10-22

Total Pages: 378

ISBN-13: 3030218104

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This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website. Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.

The Comprehension Toolkit (Ages 5-8)
The Comprehension Toolkit (Ages 5-8)

Author: Angela Ehmer

Publisher:

Published: 2019-06-10

Total Pages:

ISBN-13: 9780646802435

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Teacher reference resource containing comprehension lessons for teachers of children in the early years of school.

Cause and Correlation in Biology
Cause and Correlation in Biology

Author: Bill Shipley

Publisher: Cambridge University Press

Published: 2002-08

Total Pages: 330

ISBN-13: 9780521529211

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This book goes beyond the truism that 'correlation does not imply causation' and explores the logical and methodological relationships between correlation and causation. It presents a series of statistical methods that can test, and potentially discover, cause-effect relationships between variables in situations in which it is not possible to conduct randomised or experimentally controlled experiments. Many of these methods are quite new and most are generally unknown to biologists. In addition to describing how to conduct these statistical tests, the book also puts the methods into historical context and explains when they can and cannot justifiably be used to test or discover causal claims. Written in a conversational style that minimises technical jargon, the book is aimed at practising biologists and advanced students, and assumes only a very basic knowledge of introductory statistics.

The Book of Why
The Book of Why

Author: Judea Pearl

Publisher: Basic Books

Published: 2018-05-15

Total Pages: 432

ISBN-13: 0465097618

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A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence "Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

Encyclopedia of Research Design
Encyclopedia of Research Design

Author: Neil J. Salkind

Publisher: SAGE

Published: 2010-06-22

Total Pages: 1779

ISBN-13: 1412961270

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"Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate experiment design strategies and results. Two additional features carry this encyclopedia far above other works in the field: bibliographic entries devoted to significant articles in the history of research design and reviews of contemporary tools, such as software and statistical procedures, used to analyze results. It covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research; it addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences; it provides summaries of advantages and disadvantages of often-used strategies; and it uses hundreds of sample tables, figures, and equations based on real-life cases."--Publisher's description.

Computing in Cause-Effect Structures
Computing in Cause-Effect Structures

Author: Ludwik Czaja

Publisher:

Published: 2022

Total Pages: 0

ISBN-13: 9783030888145

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This book focuses on numerous examples of tasks represented by c-e structure. Cause-effect (c-e) structures are dynamic objects devised for algebraic and graphic description of realistic tasks. They constitute a formal system providing means to specify or implement (depending on degree of description generality) the tasks. They can be transformed, thus come under simplification, in accordance with rules-axioms of their algebra. Also, their properties can be inferred from the axioms. One objective of this book is presentation, by many realistic examples, of computing capability of c-e structures, without entering into mathematical details of their algebra. In particular, how computing with natural numbers and in propositional calculus can be performed by c-e structures and how to specify bahaviour of data structures. But also demonstration of many other tasks taken from the area of parallel processing, specified as c-e structures. Another objective is modelling or simulation by means of c-e structures, of other descriptive systems, devised for tasks from various fields. Also without formalizing by usage of functions between the systems. This concerns formalisms such as reaction systems, rough sets, Petri nets and CSP-like languages. Also on such, where temporal interdependence between actions matters. The presentation of examples is prevalently graphic, in the form of peculiar nets, but accompanied by their algebraic and set-theoretic expressions. A fairly complete exposition of concepts and properties of the algebra of cause-effect structures is in the previous book appeared in the Lecture Notes in Networks and Systems series. But basic notions of c-e structures are here provided for understanding the examples.