Author: Jan Lemeire
Publisher: ASP / VUBPRESS / UPA
ISBN: 9054874570
Category : Algorithms
Languages : en
Pages : 241

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Author: Ildar Batyrshin
Publisher: Springer Science & Business Media
ISBN: 3642253237
Category : Computers
Languages : en
Pages : 618

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Book Description
The two-volume set LNAI 7094 and LNAI 7095 constitutes the refereed proceedings of the 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, held in Puebla, Mexico, in November/December 2011. The 96 revised papers presented were carefully reviewed and selected from numerous submissions. The first volume includes 50 papers representing the current main topics of interest for the AI community and their applications. The papers are organized in the following topical sections: automated reasoning and multi-agent systems; problem solving and machine learning; natural language processing; robotics, planning and scheduling; and medical applications of artificial intelligence.

Author: Phyllis McKay Illari
Publisher: Oxford University Press
ISBN: 0199574138
Category : Mathematics
Languages : en
Pages : 953

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Book Description
Why do ideas of how mechanisms relate to causality and probability differ so much across the sciences? Can progress in understanding the tools of causal inference in some sciences lead to progress in others? This book tackles these questions and others concerning the use of causality in the sciences.

Author: Jonas Peters
Publisher: MIT Press
ISBN: 0262037319
Category : Computers
Languages : en
Pages : 289

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Book Description
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Author: University Microfilms, Incorporated
Publisher: University Microfilms
ISBN: 9780835708418
Category : Education
Languages : en
Pages : 796

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Author:
Publisher:
ISBN:
Category : Psychological abstracts
Languages : en
Pages : 804

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Author: Herbert C. Schulberg
Publisher:
ISBN:
Category : Evaluation research (Social action programs).
Languages : en
Pages : 608

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Book Description
In assembling almost three dozen papers which bear upon the evaluation of health programs, we have tried to select those materials which could meet the needs of both researchers and program directors. It is believed that in spite of the well-known differences in orientation and priorities which these two groups frequently exhibit, nevertheless there exists a wide span of common concern which needs only to be properly plumbed. To capitalize upon the mutual interests of researchers and practitioners and to demonstrate opportunities for fruitful collaboration, we have emphasized papers of potential interest to both groups and avoided selections which were overly skewed toward the needs of only one of them. Many other sources are available for the study of program development or research methodology but this volume one differs in its attempt to integrate what generally have been dichotomous fields of interest. Volume II was a response to the escalating demands for accountability, increasingly complex conceptual and methodological options, and a proliferating literature require that sophisticated materials be readily available if assessments are to be performed in knowledgeable ways. When is paired with our earlier volume, those teaching the growing number of program evaluation courses in medical schools and schools of public health, social work, and business should have more than enough reading materials for their introductory and advanced seminars.

Author: William Dale Berry
Publisher: SAGE
ISBN: 9780803922655
Category : Reference
Languages : en
Pages : 100

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Book Description
The author defines the concept of identification and explains what 'goes wrong' with some nonrecursive models to make them nonidentified. He provides various tests which can be used to determine whether a nonrecursive model is identified, and reviews common techniques for estimating the parameters of an identified model.

Author: H. M. Blalock
Publisher: Routledge
ISBN: 1351529803
Category : Social Science
Languages : en
Pages : 300

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Book Description
This is a companion volume to Causal Models in the Social Sciences, the majority of articles concern panel designs involving repeated measurements while a smaller cluster involve discussions of how experimental designs may be improved by more explicit attention to causal models. All of the papers are concerned with complications that may occur in actual research designs- as compared with idealized ones that often become the basis of textbook discussions of design issues.

Author:
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 356

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