Elements of Causal Inference: Foundations and Learning Algorithms by Jonas Peter

Description: Elements of Causal Inference by Jonas Peters, Dominik Janzing, Bernhard Schölkopf Estimated delivery 3-12 business days Format Hardcover Condition Brand New Description A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. Publisher 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 Biography Jonas Peters is Associate Professor of Statistics at the University of Copenhagen.Dominik Janzing is a Senior Research Scientist at the Max Planck Institute for Intelligent Systems in T bingen, Germany.Bernhard Sch lkopf is Director at the Max Planck Institute for Intelligent Systems in T bingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods- Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press. Details ISBN 0262037319 ISBN-13 9780262037310 Title Elements of Causal Inference Author Jonas Peters, Dominik Janzing, Bernhard Schölkopf Format Hardcover Year 2017 Pages 288 Publisher MIT Press Ltd GE_Item_ID:141685280; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys

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