Learn IT - Books tags markov

Graphical Models for Machine Learning and Digital Communication (Adaptive Computation and Machine Learning)

A Bradford Book, 1998

A variety of problems m machine learning and digital communication deal with complex but structured natural or artificial systems. Natural patterns mat we wish to automatically classify' are a consequence of a hierarchical causal physical process. Learning about the world m which we live requires mat we extract useful sensor)'...

Abstraction in Artificial Intelligence and Complex Systems

Springer, 2013

Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive...

Mastering Probabilistic Graphical Models using Python

Packt Publishing, 2015

Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python

About This Book

Gain in-depth knowledge of Probabilistic Graphical Models

Model time-series problems using Dynamic Bayesian Networks

A practical guide to...

Evolutionary Algorithms (The IMA Volumes in Mathematics and its Applications)

Springer, 1999

The IMA Workshop on Evolutionary Algorithms brought together many of the top researchers in the area of Evolutionary Computation for a week of intensive interaction. The field of Evolutionary Computation has developed significantly over the past 30 years and today consists of a variety of subfields such as genetic algorithms, evolution...

Advanced Digital Signal Processing and Noise Reduction

John Wiley & Sons, 2006

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...

Computer Vision: Models, Learning, and Inference

Cambridge University Press, 2012

There are already many computer vision textbooks, and it is reasonable to question the need for another. Let me explain why I chose to write this volume.

Computer vision is an engineering discipline; we are primarily motivated by the real-world concern of...

Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)

MIT Press, 2007

Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases, and programming languages to represent structure. In...

Pattern Recognition, Third Edition

Academic Press, 2006

A classic -- offering comprehensive and unified coverage with a balance between theory and practice!

Pattern recognition is integral to a wide spectrum of scientific disciplines and technologies including image analysis, speech recognition and audio classification, communications, computer-aided diagnosis, data mining. The authors,...

Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem

Packt Publishing, 2018

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn

Key Features

Build a variety of Hidden Markov Models (HMM)

Create and apply models to any sequence of data to analyze, predict, and extract valuable...

Recursive Methods in Economic Dynamics

Harvard Business School, 1989

A magnificent work that is bound to have immense influence on the ways economists think about dynamic systems for many years to come. My own guess is that this book will eventually acquire the stature, say, of Hicks's Value and Capital or Samuelson's Foundations.
--Thomas J. Sargent, Hoover Institution

The...

Introducing Monte Carlo Methods with R (Use R)

Springer, 2009

Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing...

A Second Course in Probability

pekozbooks, 2007

The 2006 INFORMS Expository Writing Award-winning and best-selling author Sheldon Ross (University of Southern California) teams up with Erol Peköz (Boston University) to bring you this textbook for undergraduate and graduate students in statistics, mathematics, engineering, finance, and actuarial science. This is a guided tour designed...

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