Limit-State Design of Prestressed Concrete, Volume 1: The Design of the Section

Engineering & Transportation

Guyon

Guyon, Yves

Engineering

John Wiley&Sons

Limit-State Design of Prestressed Concrete: The Design of the Member

Histoire Des Amazones Anciennes Et Modernes: Enrichie De Médailles (French Edition)

Health, Fitness & Dieting

Nabu Press

L'oracle Des Nouveaux Philosophes: Pour Servir De Suite Et D'éclaircissement Aux Oeuvres De M. De Voltaire (French Edition)

Literature & Fiction

History & Criticism

This is a reproduction of a book published before 1923. This book may have occasional imperfections such as missing or blurred pages, poor pictures, errant marks, etc. that were either part of the original artifact, or were introduced by the scanning process. We believe this work is culturally important, and despite the imperfections, have elected to bring it back into print as part of our continuing commitment to the preservation of printed works worldwide. We appreciate your understanding of the imperfections in the preservation process, and hope you enjoy this valuable book.

Esteemed as one of the greatest Christian works in history, this book effectively explains short and easy methods of prayer for those who hunger and thirst after God's presence.

Experiencing God Through Prayer

Madame

ReadaClassic.com

Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)

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.

Computers & Technology

Computer Science

Springer

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