Causality: Models, Reasoning And Inference

Causality: Models, Reasoning and Inference, 2nd Edition  eBooks & eLearning

Posted by Willson at Nov. 2, 2016
Causality: Models, Reasoning and Inference, 2nd Edition

Judea Pearl, "Causality: Models, Reasoning and Inference, 2nd Edition"
English | 2009 | ISBN: 052189560X | 484 pages | PDF | 7.3 MB

Causality: Models, Reasoning and Inference (repost)  eBooks & eLearning

Posted by libr at Feb. 29, 2016
Causality: Models, Reasoning and Inference (repost)

Causality: Models, Reasoning and Inference by Judea Pearl
English | 2009 | ISBN: 052189560X | 484 pages | EPUB | 5 MB

Causality: Models, Reasoning and Inference  

Posted by interes at Nov. 18, 2014
Causality: Models, Reasoning and Inference

Causality: Models, Reasoning and Inference by Judea Pearl
English | 2009 | ISBN: 052189560X | 484 pages | EPUB | 5 MB
Computer Vision: Models, Learning, and Inference (repost)

Computer Vision: Models, Learning, and Inference by Dr Simon J. D. Prince
English | ISBN: 1107011795 | 2012 | PDF + EPUB | 598 pages | 26 + 35 MB

Computer Vision: Models, Learning, and Inference  

Posted by viserion at June 21, 2013
Computer Vision: Models, Learning, and Inference

Dr Simon J. D. Prince, "Computer Vision: Models, Learning, and Inference"
ISBN: 1107011795 | 2012 | PDF | 598 pages | 26 MB

Computer Vision: Models, Learning, and Inference  

Posted by arundhati at Dec. 5, 2012
Computer Vision: Models, Learning, and Inference

Dr Simon J. D. Prince, "Computer Vision: Models, Learning, and Inference"
2012 | ISBN-10: 1107011795 | EPUB | 600 pages | 35 MB

Clinical Reasoning and Care Coordination in Advanced Practice Nursing  eBooks & eLearning

Posted by readerXXI at Nov. 21, 2016
Clinical Reasoning and Care Coordination in Advanced Practice Nursing

Clinical Reasoning and Care Coordination in Advanced Practice Nursing
by RuthAnne Kuiper and Daniel J. Pesut
English | 2016 | ISBN: 0826131832 | 414 Pages | True PDF | 3.79 MB

This book describes an innovative model for helping APRN students develop the clinical reasoning skills required to navigate complex patient care needs and coordination in advanced nursing practice.

Inferential Models: Reasoning with Uncertainty  

Posted by interes at Jan. 19, 2016
Inferential Models: Reasoning with Uncertainty

Inferential Models: Reasoning with Uncertainty by Ryan Martin and Chuanhai Liu
English | 2015 | ISBN: 1439886482 | 276 pages | PDF | 9 MB
Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications [Repost]

Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications By Dr. David Ardia
English | Springer (2008) | ISBN-10: 3540786562 | 206 pages | ُPDF | 7.40 MB

This book presents methodologies for the Bayesian estimation of GARCH models and their application to financial risk management. The study of these models from a Bayesian viewpoint is relatively recent and can be considered very promising due to the advantages of the Bayesian approach, in particular the possibility of obtaining small-sample results and integrating these results in a formal decision model. The first two chapters introduce the work and give an overview of the Bayesian paradigm for inference. The next three chapters describe the estimation of the GARCH model with Normal innovations and the linear regression models with conditionally Normal and Student-t-GJR errors. The sixth chapter shows how agents facing different risk perspectives can select their optimal Value at Risk Bayesian point estimate and documents that the differences between individuals can be substantial in terms of regulatory capital. The last chapter proposes the estimation of a Markov-switching GJR model.
Abductive Reasoning and Learning (Handbook of Defeasible Reasoning and Uncertainty Management Systems) by Dov M. Gabbay

Abductive Reasoning and Learning (Handbook of Defeasible Reasoning and Uncertainty Management Systems) (Volume 4) by Dov M. Gabbay
English | 2000 | ISBN: 9048155606 | 444 Pages | PDF | 14 MB

This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches. Abduction is central to all areas of applied reasoning, including artificial intelligence, philosophy of science, machine learning, data mining and decision theory, as well as logic itself.