LEARNING CAUSAL MODELS OF MULTIVARIATE SYSTEMS And the Value of it for the Performance Modeling of Computer Programs PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download LEARNING CAUSAL MODELS OF MULTIVARIATE SYSTEMS And the Value of it for the Performance Modeling of Computer Programs PDF full book. Access full book title LEARNING CAUSAL MODELS OF MULTIVARIATE SYSTEMS And the Value of it for the Performance Modeling of Computer Programs by Jan Lemeire. Download full books in PDF and EPUB format.
Author: Jan Lemeire
Publisher: ASP / VUBPRESS / UPA
ISBN: 9054874570
Category : Algorithms
Languages : en
Pages : 241
Get Book Here
Book Description
Author: Jan Lemeire
Publisher: ASP / VUBPRESS / UPA
ISBN: 9054874570
Category : Algorithms
Languages : en
Pages : 241
Get Book Here
Book Description
Author: Phyllis McKay Illari
Publisher: Oxford University Press
ISBN: 0199574138
Category : Mathematics
Languages : en
Pages : 953
Get Book Here
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: Ildar Batyrshin
Publisher: Springer Science & Business Media
ISBN: 3642253237
Category : Computers
Languages : en
Pages : 618
Get Book Here
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:
Publisher:
ISBN:
Category : Psychological abstracts
Languages : en
Pages : 804
Get Book Here
Book Description
Author: University Microfilms, Incorporated
Publisher: University Microfilms
ISBN: 9780835708418
Category : Education
Languages : en
Pages : 796
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 356
Get Book Here
Book Description
Author: Educational Resources Information Center (U.S.)
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 820
Get Book Here
Book Description
"A subject-author-institution index which provides titles and accession numbers to the document and report literature that was announced in the monthly issues of Resources in education" (earlier called Research in education).
Author: CCM Information Corporation
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 818
Get Book Here
Book Description
Author: Jonas Peters
Publisher: MIT Press
ISBN: 0262037319
Category : Computers
Languages : en
Pages : 289
Get Book Here
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: Ivo D. Dinov
Publisher: Springer Nature
ISBN: 3031174836
Category : Computers
Languages : en
Pages : 940
Get Book Here
Book Description
This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings. Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book’s fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.
