Bayesian nonparametrics for causal inference and missing data

Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the impor...

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Библиографические подробности
Главные авторы: Daniels, M. J. (Michael Joseph) (Автор), Linero, Antonio (Автор), Roy, Jason (Jason Allen) (Автор)
Формат:
Язык:английский
Опубликовано: Boca Raton, FL CRC Press, an imprint of Taylor & Francis Group, LLC 2024.
Серии:Chapman & Hall/CRC monographs on statistics & applied probability 173
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