Advances in Bayesian networks /
Includes the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. Presents specific topics such as approximate propagation, abductive inferences, decision graphs and applications of influence -- Back cover.
| Corporate Author: | SpringerLink (Online service) |
|---|---|
| Other Authors: | Gámez, José A., Moral, Serafín, 1952-, Salmerón, Antonio |
| Format: | eBook |
| Language: | English |
| Published: |
Berlin :
Springer,
[2010]
|
| Series: | Studies in fuzziness and soft computing ;
volume 146. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Similar Items
Advances in Bayesian networks /
Published: (2004)
Published: (2004)
Bayesian networks and decision graphs /
by: Jensen, Finn V.
Published: (2001)
by: Jensen, Finn V.
Published: (2001)
Bayesian artificial intelligence /
by: Korb, Kevin B.
Published: (2004)
by: Korb, Kevin B.
Published: (2004)
Learning Bayesian networks /
by: Neapolitan, Richard E.
Published: (2004)
by: Neapolitan, Richard E.
Published: (2004)
Approximation methods for efficient learning of Bayesian networks /
by: Riggelsen, Carsten
Published: (2008)
by: Riggelsen, Carsten
Published: (2008)
Bayesian networks and decision graphs /
by: Jensen, Finn V.
Published: (2001)
by: Jensen, Finn V.
Published: (2001)
Bayesian networks : an introduction /
by: Koski, Timo
Published: (2009)
by: Koski, Timo
Published: (2009)
Bayesian networks : an introduction /
by: Koski, Timo
Published: (2009)
by: Koski, Timo
Published: (2009)
Bayesian nonparametrics via neural networks /
by: Lee, Herbert K. H.
Published: (2004)
by: Lee, Herbert K. H.
Published: (2004)
Statistical and evolutionary analysis of biological networks /
Published: (2010)
Published: (2010)
Bayesian optimization /
by: Garnett, Roman
Published: (2023)
by: Garnett, Roman
Published: (2023)
Machine learning with neural networks : an introduction for scientists and engineers /
by: Mehlig, Bernhard, 1964-
Published: (2021)
by: Mehlig, Bernhard, 1964-
Published: (2021)
Bayesian network technologies : applications and graphical models /
Published: (2007)
Published: (2007)
TensorFlow shen du xue xi ke cheng : shen du shen jing wang luo zai ji qi xue ren wu de ying yong.
Published: (2017)
Published: (2017)
Artificial neural networks : learning algorithms, performance evaluation, and applications /
by: Karayiannis, N. B. (Nicolaos B.), 1960-
Published: (2011)
by: Karayiannis, N. B. (Nicolaos B.), 1960-
Published: (2011)
Adaptive learning of polynomial networks : genetic programming, backpropagation and Bayesian methods /
by: Nikolaev, Nikolay Y.
Published: (2006)
by: Nikolaev, Nikolay Y.
Published: (2006)
An introduction to Bayesian inference.
Published: (2017)
Published: (2017)
Understanding computational Bayesian statistics /
by: Bolstad, William M., 1943-
Published: (2010)
by: Bolstad, William M., 1943-
Published: (2010)
Principles and labs for deep learning /
by: Huang, Shih-Chia
Published: (2021)
by: Huang, Shih-Chia
Published: (2021)
Bayesian data analysis /
by: Gelman, Andrew
Published: (2014)
by: Gelman, Andrew
Published: (2014)
Applying the idiomatic design pattern to convolutional neural networks /
Published: (2020)
Published: (2020)
Bayesian modeling and computation in Python /
by: Martin, Osvaldo
Published: (2022)
by: Martin, Osvaldo
Published: (2022)
PAC-Bayesian supervised classification : the thermodynamics of statistical learning /
by: Catoni, Olivier
Published: (2007)
by: Catoni, Olivier
Published: (2007)
Bayesian statistics 7 /
Published: (2023)
Published: (2023)
Shu ju fen xi yu ji qi xue xi ji chu.
Published: (2019)
Published: (2019)
Bayesian brain : probabilistic approaches to neural coding /
Published: (2007)
Published: (2007)
PyTorch recipes : A Problem-Solution Approach to Build, Train and Deploy Neural Network Models /
by: Mishra, Pradeepta
Published: (2022)
by: Mishra, Pradeepta
Published: (2022)
Machine learning : from theory to applications : cooperative research at Siemens and MIT /
Published: (1993)
Published: (1993)
Machine learning avec R
by: Burger, Scott V.
Published: (2018)
by: Burger, Scott V.
Published: (2018)
Concepts and techniques of graph neural network /
Published: (2023)
Published: (2023)
From Synapses to Rules : Discovering Symbolic Rules from Neural Processed Data /
by: Apolloni, Bruno, 1946-
Published: (2002)
by: Apolloni, Bruno, 1946-
Published: (2002)
Gradient expectations : structure, origins, and synthesis of predictive neural networks /
by: Downing, Keith L.
Published: (2023)
by: Downing, Keith L.
Published: (2023)
Deep learning with R /
by: Chollet, François, et al.
Published: (2022)
by: Chollet, François, et al.
Published: (2022)
Understanding computational Bayesian statistics /
by: Bolstad, William M., 1943-
Published: (2010)
by: Bolstad, William M., 1943-
Published: (2010)
Bayes Rules! : an Introduction to Applied Bayesian Modeling.
by: Johnson, Alicia A.
Published: (2022)
by: Johnson, Alicia A.
Published: (2022)
Bayesian Modelling of Spatio-Temporal Data with R.
by: Sahu, Sujit Kumar
Published: (2022)
by: Sahu, Sujit Kumar
Published: (2022)