Learn IT - Books tags markov

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,...

Guide to Medical Image Analysis: Methods and Algorithms (Advances in Computer Vision and Pattern Recognition)

Springer, 2012

This book presents a comprehensive overview of medical image analysis. Practical in approach, the text is uniquely structured by potential applications. Features: presents learning objectives, exercises and concluding remarks in each chapter, in addition to a glossary of abbreviations; describes a range of common imaging techniques,...

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...

Data Algorithms: Recipes for Scaling Up with Hadoop and Spark

O'Reilly, 2015

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...

Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents

Packt Publishing, 2018

A practical guide to mastering reinforcement learning algorithms using Keras

Key Features

Build projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into action

Get to grips with Keras and practice on real-world unstructured...

Markov Models for Pattern Recognition: From Theory to Applications

Springer, 2007

The development of pattern recognition methods on the basis of so-called Markov models is tightly coupled to the technological progress in the field of automatic speech recognition. Today, however, Markov chain and hidden Markov models are also applied in many other fields where the task is the modeling and analysis of chronologically...

Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)

Springer, 2010

Time Series Analysis and Its Applications presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception...

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...

Optimization, Control, and Applications of Stochastic Systems: In Honor of Onésimo Hernández-Lerma (Systems & Control: Foundations & Applications)

Birkhauser, 2012

This volume provides a general overview of discrete- and continuous-time Markov control processes and stochastic games, along with a look at the range of applications of stochastic control and some of its recent theoretical developments. These topics include various aspects of dynamic programming, approximation...

Numerical Issues in Statistical Computing for the Social Scientist (Wiley Series in Probability and Statistics)

John Wiley & Sons, 2003

At last—a social scientist's guide through the pitfalls of modern statistical computing
Addressing the current deficiency in the literature on statistical methods as they apply to the social and behavioral sciences, Numerical Issues in Statistical Computing for the Social Scientist seeks to provide readers with a unique practical...

Fuzzy Probabilities: New Approach and Applications (Studies in Fuzziness and Soft Computing)

Springer, 1999

In probability and statistics we often have to estimate probabilities and parameters in probability distributions using a random sample. Instead of using a point estimate calculated from the data we propose using fuzzy numbers which are constructed from a set of confidence intervals. In probability calculations we apply constrained fuzzy arithmetic...

Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala

Packt Publishing, 2018

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.

Key Features

Explore machine learning techniques with prominent open source Scala libraries such as Spark ML, H2O, MXNet, Zeppelin, and DeepLearning4j

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