Learn IT - Books tags markov

Introduction to Machine Learning (Adaptive Computation and Machine Learning)

MIT Press, 2004

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed...

Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support

Cambridge University Press, 2005

Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book...

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...

Flowgraph Models for Multistate Time-to-Event Data (Wiley Series in Probability and Statistics)

John Wiley & Sons, 2004

The main purpose of this book is to present an introduction to flowgraph models for time-to-event data. The focus is on stochastic models for censored time-to-event data with competing risks and recurrent events. The applications are geared to survivalanalysis and reliability. I view flowgraph models as providing a methodology for data...

Markovian Demand Inventory Models (International Series in Operations Research & Management Science)

Springer, 2009

Inventory management is concerned with matching supply with demand and a central problem in Operations Management. The problem is to find the amount to be produced or purchased in order to maximize the total expected profit or minimize the total expected cost. Over the past two decades, several variations of the formula appeared, mostly in trade...

Markov Networks in Evolutionary Computation (Adaptation, Learning, and Optimization)

Springer, 2012

Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of machine learning methods in...

Linear Algebra and Probability for Computer Science Applications

CRC Press, 2012

Based on the author’s course at NYU, Linear Algebra and Probability for Computer Science Applications gives an introduction to two mathematical fields that are fundamental in many areas of computer science. The course and the text are addressed to students with a very weak mathematical background. Most of the...

Performance by Design: Computer Capacity Planning By Example

Prentice Hall, 2004

Practical systems modeling: planning performance, availability, security, and more

Computing systems must meet increasingly strict Quality of Service (QoS) requirements for performance, availability, security, and maintainability. To achieve these goals, designers, analysts, and capacity planners need a far more thorough understanding of...

Basic Concepts in Computational Physics

Springer, 2013

With the development of ever more powerful computers a new branch of physics and engineering evolved over the last few decades: Computer Simulation or Computational Physics. It serves two main purposes:

- Solution of complex mathematical problems such as, differential equations, minimization/optimization, or high-dimensional...

Analysis of Computer and Communication Networks

Springer, 2008

This new work presents the mathematical theory and techniques for analyzing and modeling high-performance global communication networks, with a focus on software employed at the end nodes and intermediate switches.

Topics include, but are not limited to Markov chains and queuing analysis, traffic modeling, interconnection networks, and switch...

Pattern Recognition, Second Edition

Academic Press, 2003

Pattern recognition is becoming increasingly important in the age of automation and information handling and retrieval.
This book provides the most comprehensive treatment available of pattern recognition, from an engineering perspective. Developed through more than ten years of teaching experience, Pattern Recognition is appropriate for both...

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