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Probability: With Applications and R
Probability: With Applications and R

An introduction to probability at the undergraduate level

Chance and randomness are encountered on a daily basis. Authored by a highly qualified professor in the field, Probability: With Applications and R delves into the theories and applications essential to obtaining a thorough understanding of probability.

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New Directions in Statistical Signal Processing: From Systems to Brains (Neural Information Processing)
New Directions in Statistical Signal Processing: From Systems to Brains (Neural Information Processing)
Signal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophisticated kinds of signal processing and elaborate hierachical levels of neural computation...
Advanced Digital Signal Processing and Noise Reduction
Advanced Digital Signal Processing and Noise Reduction
Signal processing plays an increasingly central role in the development of modern telecommunication and information processing systems, with a wide range of applications in areas such as multimedia technology, audio-visual signal processing, cellular mobile communication, radar systems and financial data forecasting. The theory and application of...
Reconstructing Evolution: New Mathematical and Computational Advances
Reconstructing Evolution: New Mathematical and Computational Advances
Evolution is a complex process, acting at multiple scales, from DNA sequences and proteins to populations of species. Understanding and reconstructing evolution is of major importance in numerous subfields of biology. For example, phylogenetics and sequence evolution is central to comparative genomics, attempts to decipher genomes, and molecular...
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...

Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
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...
Linear Algebra and Probability for Computer Science Applications
Linear Algebra and Probability for Computer Science Applications

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...

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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Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning)
Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning)

We have been very pleased, beyond our expectations, with the reception of the first edition of this book. Bioinformatics, however, continues to evolve very rapidly, hence the need for a new edition. In the past three years, fullgenome sequencing has blossomed with the completion of the sequence of the fly and the first draft of the...

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,...
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...

Signals and Boundaries: Building Blocks for Complex Adaptive Systems
Signals and Boundaries: Building Blocks for Complex Adaptive Systems

Complex adaptive systems (cas), including ecosystems, governments, biological cells, and markets, are characterized by intricate hierarchical arrangements of boundaries and signals. In ecosystems, for example, niches act as semi-permeable boundaries, and smells and visual patterns serve as signals; governments have departmental hierarchies...

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