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Anotace knihy

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.

Parametry knihy

Zařazení knihy Knihy v angličtině Computing & information technology Computer science Artificial intelligence

• Plný název: Elements of Causal Inference
• Autor: Jonas Peters, Dominik Janzing, Bernhard Scholkopf
• Jazyk: Angličtina
• Vazba: Pevná
• Počet stran: 288
• EAN: 9780262037310
• ISBN: 9780262037310
• ID: 16203047
• Nakladatelství: MIT Press Ltd
• Hmotnost: 718 g
• Rozměry: 183 × 236 × 24 mm
• Datum vydání: 29. November 2017

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