Learn IT - Books tags inference

Understanding Computational Bayesian Statistics (Wiley Series in Computational Statistics)

John Wiley & Sons, 2009

In theory, Bayesian statistics is very simple. The posterior is proportional to the prior times likelihood. This gives the shape of the posterior, but it is not a density so it cannot be used for inference. The exact scale factor needed to make this a density can be found only in a few special cases. For other cases, the scale...

Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction (Applications of Mathematics, 27)

Springer, 1995

This second edition of G. Winkler's successful book on random field approaches to image analysis, related Markov Chain Monte Carlo methods, and statistical inference with emphasis on Bayesian image analysis concentrates more on general principles and models and less on details of concrete applications. Addressed to students and scientists from...

Intelligent Information Integration for the Semantic Web (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)

Springer, 2005

The Semantic Web offers new options for information processes. Dr. Visser is dealing with two core issues in this area: the integration of data on the semantic level and the problem of spatio-temporal representation and reasoning. He tackles existing research problems within the field of geographic information systems (GIS), the solutions of...

Introductory Biostatistics

John Wiley & Sons, 2003

The nuts-and-bolts of biostatistics–without the anxiety

To students in public health, dentistry, nursing, and other health disciplines, the introductory course in biostatistics conjures up dread images of mind-numbing formulas and dry-as-bones texts. This text, adapted from and expanding on his bestselling Health and Numbers (Wiley), will...

Regression Analysis: Statistical Modeling of a Response Variable

Academic Press, 2006

The objective of Regression Analysis: Statistical Modeling of a Response Variable, Second Edition, is to provide tools necessary for using the modeling approach for the intelligent statistical analysis of a response variable. Although there is strong emphasis on regression analysis, there is coverage of other linear models such...

Representations and Techniques for 3D Object Recognition & Scene Interpretation

Morgan and Claypool Publishers, 2011

3D scene understanding and object recognition are among the grandest challenges in computer vision. A wide variety of techniques and goals, such as structure from motion, optical flow, stereo, edge detection, and segmentation, could be viewed as subtasks within scene understanding and recognition. Many of these applicable methods are...

Combinatorial Search: From Algorithms to Systems

Springer, 2013

Although they are believed to be unsolvable in general, tractability results suggest that some practical NP-hard problems can be efficiently solved. Combinatorial search algorithms are designed to efficiently explore the usually large solution space of these instances by reducing the search space to feasible regions and using heuristics to...

Using R for Data Analysis in Social Sciences

Oxford University Press, 2018

Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate...

Qualitative Representation of Spatial Knowledge (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)

Springer, 1994

Cognitive spatial concepts are qualitative in nature, i.e., they are based not so much on exact quantities but on comparisons between perceived magnitudes. We develop a qualitative model for the representation of spatial knowledge (in particular, of positional information about 2-dimensional projections) that is based only on locative...

Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach

Cambridge University Press, 2002

This book investigates the opportunities in building intelligent decision support systems offered by multiagent distributed probabilistic reasoning.

Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become an active field of research and practice in artificial intelligence, operations...

Random Matrix Methods for Wireless Communications

Cambridge University Press, 2011

Blending theoretical results with practical applications, this book provides an introduction to random matrix theory and shows how it can be used to tackle a variety of problems in wireless communications. The Stieltjes transform method, free probability theory, combinatoric approaches, deterministic equivalents and spectral analysis methods...

A Handbook of Statistical Analyses Using R, Second Edition

CRC Press, 2009

A Proven Guide for Easily Using R to Effectively Analyze Data

Like its bestselling predecessor, A Handbook of Statistical Analyses Using R, Second Edition provides a guide to data analysis using the R system for statistical computing. Each chapter includes a brief account of the relevant statistical...

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