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Probability and Statistics for Computer Scientists
Probability and Statistics for Computer Scientists

Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools
Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic
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Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)

Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis

 

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely...

Mathematical Models of Spoken Language
Mathematical Models of Spoken Language
Humans use language to convey meaningful messages to each other. Linguistic competence consists in the ability to express meaning reliably, not simply to obtain faithful lexical transcriptions. This invaluable reference tool is the product of many years' experience and research on language and speech technology. It presents the motivations for,...
Optimisation in Signal and Image Processing
Optimisation in Signal and Image Processing

This book describes the optimization methods most commonly encountered in signal and image processing: artificial evolution and Parisian approach; wavelets and fractals; information criteria; training and quadratic programming; Bayesian formalism; probabilistic modeling; Markovian approach; hidden Markov models; and metaheuristics (genetic...

The History of Approximation Theory: From Euler to Bernstein
The History of Approximation Theory: From Euler to Bernstein

The problem of approximating a given quantity is one of the oldest challenges faced by mathematicians. Its increasing importance in contemporary mathematics has created an entirely new area known as Approximation Theory. The modern theory was initially developed along two divergent schools of thought: the Eastern or Russian group, employing...

Fundamentals of Speech Recognition
Fundamentals of Speech Recognition

Provides a theoretically sound, technically accurate, and complete description of the basic knowledge and ideas that constitute a modern system for speech recognition by machine. Covers production, perception, and acoustic-phonetic characterization of the speech signal; signal processing and analysis...

Statistical Approach to Quantum Field Theory: An Introduction (Lecture Notes in Physics)
Statistical Approach to Quantum Field Theory: An Introduction (Lecture Notes in Physics)

Over the past few decades the powerful methods of statistical physics and Euclidean quantum field theory have moved closer together, with common tools based on the use of path integrals. The interpretation of Euclidean field theories as particular systems of statistical physics has opened up new avenues for understanding strongly coupled...

Data Mining Methods and Models
Data Mining Methods and Models
Apply powerful Data Mining Methods and Models to Leverage your Data for Actionable Results

Data Mining Methods and Models provides:

  • The latest techniques for uncovering hidden nuggets of information
  • The insight into how the data mining algorithms actually work
  • The hands-on experience of...
Bioinformatics: High Performance Parallel Computer Architectures (Embedded Multi-Core Systems)
Bioinformatics: High Performance Parallel Computer Architectures (Embedded Multi-Core Systems)

New sequencing technologies have broken many experimental barriers to genome scale sequencing, leading to the extraction of huge quantities of sequence data. This expansion of biological databases established the need for new ways to harness and apply the astounding amount of available genomic information and convert it into substantive...

Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks

Bayesian networks have received a lot of attention over the last few decades from both scientists and engineers, and across a number of fields, including artificial intelligence (AI), statistics, cognitive science, and philosophy.

Perhaps the largest impact that Bayesian networks have had is on the field of AI, where they were...

Matrix Methods, Third Edition: Applied Linear Algebra
Matrix Methods, Third Edition: Applied Linear Algebra

Matrix Methods: Applied Linear Algebra, 3e, as a textbook, provides a unique and comprehensive balance between the theory and computation of matrices. The application of matrices is not just for mathematicians. The use by other disciplines has grown dramatically over the years in response to the rapid changes in technology. Matrix methods is...

Data Algorithms: Recipes for Scaling Up with Hadoop and Spark
Data Algorithms: Recipes for Scaling Up with Hadoop and Spark

If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as...

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