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Pierre Brémaud: An Introduction to Applied Probability, Buch

Pierre Brémaud
An Introduction to Applied Probability

Texts in Applied Mathematics -Band 77

This book provides the elements of probability and stochastic processes of direct interest to the applied sciences where probabilistic models play an important role, most notably in the information and co…

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EUR 74,66*
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Michael Hagmann: Validity, Reliability, and Significance, Buch

Michael Hagmann, Stefan Riezler
Validity, Reliability, and Significance

Synthesis Lectures on Human Language Technologies

This book introduces empirical methods for machine learning with a special focus on applications in natural language processing (NLP) and data science. The authors present problems of validity, reliabilit…

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EUR 42,66*
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Peter Zörnig: Probability Theory and Statistics with Real World Applications, Buch

Peter Zörnig
Probability Theory and Statistics with Real World Applications

De Gruyter Textbook

The idea of the book is to present a text that is useful for both students of quantitative sciences and practitioners who work with univariate or multivariate probabilistic models. Since the text should a…

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EUR 62,76*
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Lei Cheng: Bayesian Tensor Decomposition for Signal Processing and Machine Learning, Buch

Lei Cheng, Yik-Chung Wu, Zhongtao Chen
Bayesian Tensor Decomposition for Signal Processing and Machine Learning

This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which ach…

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EUR 142,37*
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Jakub M. Tomczak: Deep Generative Modeling, Buch

Jakub M. Tomczak
Deep Generative Modeling

This first comprehensive book on models behind Generative AI has been thoroughly revised to cover all major classes of deep generative models: mixture models, Probabilistic Circuits, Autoregressive Models…

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EUR 63,99*
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