





Probability and Statistics for Engineering and the Sciences
This marketleading text provides a comprehensive introduction to probability and statistics for engineering students in all specialties. Proven, accurate, and lauded for its excellent examples, Probability and Statistics for Engineering and the Sciences evidences Jay Devore's reputation as an outstanding author and leader in the...   Intelligent Data AnalysisThis monograph is a detailed introductory presentation of the key classes of intelligent data analysis methods. The ten coherently written chapters by leading experts provide complete coverage of the core issues.
The first half of the book is devoted to the discussion of classical statistical issues, ranging from the basic concepts of...   Fundamentals of Probability: With Stochastic Processes
"The 4th edition of Ghahramani's book is replete with intriguing historical notes, insightful comments, and wellselected examples/exercises that, together, capture much of the essence of probability. Along with its Companion Website, the book is suitable as a primary resource for a first course in probability. Moreover, it... 

Artificial Intelligence: A Modern Approach (3rd Edition)
Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is...   RealTime Volume Graphics
IN TRADITIONAL COMPUTER GRAPHICS, 3D objects are created using highlevel
surface representations such as polygonal meshes, NURBS (nonuniform
rational Bspline) patches, or subdivision surfaces. Using this modeling
paradigm, visual properties of surfaces, such as color, roughness, and
reflectance, are described by means of a shading...   Random Processes by Example
This volume first introduces the mathematical tools necessary for understanding and working with a broad class of applied stochastic models. The toolbox includes Gaussian processes, independently scattered measures such as Gaussian white noise and Poisson random measures, stochastic integrals, compound Poisson, infinitely divisible and stable... 


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