Suchergebnisse - Bayesian Analysis with Python Second Edition

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141

Introducing Monte Carlo methods with R

Robert, Christian P. (Verfasser)

New York, NY : Springer, 2010

Buch

142

Handbook of Blind Source Separation

Comon, Pierre (Verfasser)

1st edition

[Erscheinungsort nicht ermittelbar] : Elsevier Science, 2010

E-Book

143

Julia 1.0 Programming Complete Reference Guide

Balbaert, Ivo (Verfasser)

1st edition

[Erscheinungsort nicht ermittelbar] : Packt Publishing, 2019

E-Book

144

Machine Learning in Production: : Developing and Optimizing Data Science Workflows and Applications, First Edition

Kelleher, Andrew (Verfasser)

1st edition

[Erscheinungsort nicht ermittelbar] : Addison-Wesley Professional, 2019

E-Book

145

Image analysis, random fields and Markov chain Monte Carlo methods : a mathematical introduction

Winkler, Gerhard

2. ed.

Berlin : Springer, 2003

Buch

146

Data mining : practical machine learning tools and techniques

Witten, Ian H. (Verfasser)

Fourth edition

Amsterdam ; Boston ; Heidelberg [und 9 weitere] : MK Morgan Kaufmann, [2017]

Buch

147

Beginning Data Science in R 4 : Data Analysis, Visualization, and Modelling for the Data Scientist

Mailund, Thomas (Verfasser)

2nd ed. 2022

Berkeley, CA : Apress, 2022

E-Book

148

Principles of Food Sanitation

Marriott, Norman G. (Verfasser)

Fifth Edition

Boston, MA : Springer US, 2006

E-Book

149

Mechanics of solid polymers : theory and computational modeling

Bergström, Jörgen (Verfasser)

First edition

Amsterdam ; Boston ; Heidelberg ; London : Elsevier, William Andrew, 2015

Buch

150

Bridging the Gap Between AI and Reality : Second International Conference, AISoLA 2024, Crete, Greece, October 30 – November 3, 2024, Proceedings

Steffen, Bernhard (Herausgeber)

1st ed. 2025

Cham : Springer Nature Switzerland, 2025

E-Book

151

Web data mining : exploring hyperlinks, contents, and usage data

Liu, Bing (Verfasser)

2. edition

Berlin : Springer, 2011

Buch

152

Statistics for spatial data

Cressie, Noel A. C. (Verfasser)

Rev. edition

New York, NY : Wiley, 1993

Buch

153

15 MATH CONCEPTS EVERY DATA SCIENTIST SHOULD KNOW : understand and learn how to apply the math behind data science algorithms

Hoyle, David (Verfasser)

1st edition

Birmingham, UK : Packt Publishing Ltd., 2024

E-Book

154

Time series : theory and methods

Brockwell, Peter J.

2. ed., 12. pr.

New York [u.a.] : Springer, 2006

Buch

155

Data-driven science and engineering : machine learning, dynamical systems, and control

Brunton, Steven L. (Verfasser)

Cambridge : Cambridge University Press, [2021]

E-Book

156

The R book

Crawley, Michael J.

2. ed.

Chichester : Wiley, 2013

Buch

157

Log-linear models and logistic regression

Christensen, Ronald

2. ed.

New York [u.a.] : Springer, 1997

Buch

158

Business Process Management Workshops : BPM 2008 International Workshops, Milano, Italy, September 1-4, 2008, Revised Papers

Ardagna, Danilo (Herausgeber)

1st ed. 2009

Berlin, Heidelberg : Springer Berlin Heidelberg, 2009

E-Book

159

Data Fusion : Concepts, Ideas and Deep Learning

Mitchell, Harvey B (Verfasser)

3rd ed.

Berlin, Heidelberg : Springer Berlin Heidelberg, 2026

E-Book

160

Fundamentals of Pattern Recognition and Machine Learning

Braga-Neto, Ulisses (Verfasser)

2nd ed. 2024

Cham : Springer International Publishing, 2024

E-Book

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