Artificial Intelligence and Causal Inference

Lingua: inglese

Editore: Taylor & Francis, Chapman And Hall/CRC, 2022

0367859408 / 9780367859404

Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

Venditore con 5 stelle

Venditore AbeBooks dal 14 agosto 2006

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nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal forgraduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine.Key Features:Cover three types of neural networks, formulate deep learning as an optimal control problem and use Pontryagin's Maximum Principle for network training.Deep learning for nonlinear mediation and instrumental variable causal analysis.Construction of causal networks is formulated as a continuous optimization problem.Transformer and attention are used to encode-decode graphics. RL is used to infer large causal networks.Use VAE, GAN, neural differential equations, recurrent neural network (RNN) and RL to estimate counterfactual outcomes.AI-based methods for estimation of individualized treatment effect in the presence of network interference.

Codice articolo 9780367859404

Titolo
Artificial Intelligence and Causal Inference
Autore
Momiao Xiong
Editore
Taylor & Francis, Chapman And Hall/CRC
Anno di pubblicazione
2022
Condizione
Neu
Rilegatura
Buch
Lingua
inglese
ISBN 10
0367859408
ISBN 13
9780367859404
Peso dell'articolo
1520 grammi
Dimensioni
280x210x25 mm

AHA-BUCH GmbH

Einbeck, Germania

Venditore con 5 stelle

Venditore AbeBooks dal 14 agosto 2006

Tariffe di spedizione da Germania a U.S.A.

ArticoloDa 7 a 10 giorni lavorativiDa 5 a 7 giorni lavorativi
Primo articoloEUR 41,14EUR 48,74
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