Learn IT - Books tags bayesian

Probabilistic Modelling in Bioinformatics and Medical Informatics

Springer, 2004

Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these...

Heuristic and Optimization for Knowledge Discovery

Idea Group Publishing, 2002

With the large amount of data stored by many organizations, capitalists have observed that this information is an intangible asset. Unfortunately, handling large databases is a very complex process and traditional learning techniques are expensive to use. Heuristic techniques provide much help in this arena, although little is known about heuristic...

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

MIT Press, 2009

Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning...

Applied Bayesian Statistics: With R and OpenBUGS Examples (Springer Texts in Statistics)

Springer, 2013

This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics,...

Bayesian Reasoning and Machine Learning

Cambridge University Press, 2012

We live in a world that is rich in data, ever increasing in scale. This data comes from many dierent sources in science (bioinformatics, astronomy, physics, environmental monitoring) and commerce (customer databases, nancial transactions, engine monitoring, speech recognition, surveillance, search). Possessing the knowledge as to...

Advances in Mathematical Modeling for Reliability

IOS Press, 2008

Advances in Mathematical Modeling for Reliability discusses fundamental issues on mathematical modeling in reliability theory and its applications. Beginning with an extensive discussion of graphical modeling and Bayesian networks, the focus shifts towards repairable systems: a discussion about how sensitive availability calculations parameter...

High-Level Data Fusion

Artech House Publishers, 2008

Master cutting-edge Level 2 fusion techniques that help you develop powerful situation assessment services with eye-popping capabilities and performance with this trail-blazing resource. The book explores object and situation fusion processes with an appropriate handling of uncertainties, and applies cutting-edge artificial intelligence and...

Think Bayes

O'Reilly, 2013

If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you...

Python for Finance: Analyze Big Financial Data

O'Reilly, 2014

The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects...

Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

Packt Publishing, 2018

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Key Features

Explore statistics and complex mathematics for data-intensive applications

Discover new developments in EM algorithm, PCA, and bayesian...

Wireless Communication Systems: Advanced Techniques for Signal Reception

Prentice Hall, 2003

A unified framework for using today's most advanced signal processing techniques

Driven by the rapidly escalating capacity demands of emerging wireless systems, researchers havedeveloped a wide array of novel signal processing techniques for use in such systems. Now, twoleading researchers synthesize the field's vast new...

Bayesian Computation with R (Use R)

Springer, 2009

The book is a concise presentation of a wide range of Bayesian inferential problems and the computational methods to solve them. The detailed and thorough presentation style, with complete R code for the examples, makes it a welcome companion to a theoretical text on Bayesian inference.... Smart students of statistics will want to have both R and...

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