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Fundamentals of Physics and Chemistry of the Atmosphere
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Matrix Mathematics - Theory, Facts, and Formulas, Second Edition
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Additive and Cancellative Interacting Particle Systems (Lecture Notes in Mathematics) by David Griffeath (Repost)
PI and the AGM: A Study in Analytic Number Theory and Computational Complexity by Jonathan M. Borwein (Repost)
Advances in Complex Function Theory (Lecture Notes in Mathematics) by W. E. Kirwan (Repost)
Synthetic inorganic chemistry a course of laboratory and classroom study by Arthur Alphonzo Blanchard (Repost)
Invitation to Law and Society - An Introduction to the Study of Real Law
Matrix Mathematics - Theory, Facts, and Formulas, Second Edition
Mathematics Probability, Markov Chains, Queues, and Simulation - The Mathematical Basis of Performance Modeling
-Physics, Topology, Logic and Computation: A Rosetta Stone- by John C. Baez adn Mike Stay
-Quantum Physics for Scientists and Technologists- by Paul Sanghera (Repost)
Algebraic Aspects of Cryptography (Algorithms and Computation in Mathematics) by Neal Koblitz (Repost)
Mathematics Mathematical Foundations of Computer Science 2004 [Repost]
Mathematics Mathematical Logic for Computer Science (3rd edition)
Physics Democracy and Public Space: The Physical Sites of Democratic Performance
Spaces of Holomorphic Functions in the Unit Ball (Graduate Texts in Mathematics) by Kehe Zhu (Repost)
An Introduction to Ergodic Theory (Graduate Texts in Mathematics) by Peter Walters (Repost)
Statistical and Thermal Physics - With Computer Applications
Introduction to the Physics of the Earth's Interior (Cambridge Topics in Mineral Ph) by Jean Paul Poirier (Repost)
Mathematics Symmetry Theory in Molecular Physics with Mathematica: A new kind of tutorial book (Repost)
THE GNOMON WORKSHOP CREATING A 3D FIGURE STUDY BOOKWARE ISO-LZ0
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Study Modeling and Reasoning with Bayesian Networks
Posted on 2010-03-16
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More Modeling and Reasoning with Bayesian Networks This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.
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