




Bayesian Artificial Intelligence, Second Edition
Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian...     Probability and Statistics for Computer Scientists
StudentFriendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools
Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic... 


Markov Random Fields for Vision and Image Processing (MIT Press)
This volume demonstrates the power of the Markov random field (MRF) in vision, treating the MRF both as a tool for modeling image data and, utilizing recently developed algorithms, as a means of making inferences about images. These inferences concern underlying image and scene structure as well as solutions to such problems as image...   Building Probabilistic Graphical Models with Python
Solve machine learning problems using probabilistic graphical models implemented in Python with realworld applications
About This Book

Stretch the limits of machine learning by learning how graphical models provide an insight on particular problems, especially in high dimension areas such as image...
  Swift Pocket Reference
Get quick answers for developing and debugging applications with Swift, Apple’s multiparadigm programming language. This pocket reference is the perfect onthejob tool for learning Swift’s modern language features, including type safety, generics, type inference, closures, tuples, automatic memory management, and... 


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