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Markov Chains: Models, Algorithms and Applications (International Series in Operations Research & Management Science)
Markov Chains: Models, Algorithms and Applications (International Series in Operations Research & Management Science)
Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order...
Hidden Markov Models for Time Series: An Introduction Using R (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
Hidden Markov Models for Time Series: An Introduction Using R (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Reveals How HMMs Can Be Used as General-Purpose Time Series Models

Implements all methods in R
Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types, from continuous-valued, circular, and
...

Randomized Algorithms for Analysis and Control of Uncertain Systems: With Applications (Communications and Control Engineering)
Randomized Algorithms for Analysis and Control of Uncertain Systems: With Applications (Communications and Control Engineering)

The presence of uncertainty in a system description has always been a critical issue in control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain Systems, with Applications (Second Edition) is to introduce the reader to the fundamentals of probabilistic methods in the analysis and design of...

The Blender Python API: Precision 3D Modeling and Add-on Development
The Blender Python API: Precision 3D Modeling and Add-on Development

Understand Blender's Python API to allow for precision 3D modeling and add-on development. Follow detailed guidance on how to create precise geometries, complex texture mappings, optimized renderings, and much more.

This book is a detailed, user-friendly guide to understanding and using Blender’s Python API for...

Numerical Computing with Python: Harness the power of Python to analyze and find hidden patterns in the data
Numerical Computing with Python: Harness the power of Python to analyze and find hidden patterns in the data

Understand, explore, and effectively present data using the powerful data visualization techniques of Python

Key Features

  • Use the power of Pandas and Matplotlib to easily solve data mining issues
  • Understand the basics of statistics to build powerful predictive data...
Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)
Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)

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...

Linear Mixed-Effects Models Using R: A Step-by-Step Approach
Linear Mixed-Effects Models Using R: A Step-by-Step Approach
Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data include clustered observations, repeated measurements, longitudinal measurements, multivariate observations, etc.

The aim of our book is to help readers in fitting LMMs using R
...
Schaum's Outline of Mathematica, 2ed (Schaum's Outline Series)
Schaum's Outline of Mathematica, 2ed (Schaum's Outline Series)
Master Mathematica with Schaum's, the high-performance study guide. It will help you cut study time, hone problem-solving skills, and achieve your personal best on exams and projects!

Students love Schaum's Outlines because they produce results. Each year, hundreds of thousands of students improve their test scores and final grades with these...

Intelligent Systems: Approximation by Artificial Neural Networks (Intelligent Systems Reference Library)
Intelligent Systems: Approximation by Artificial Neural Networks (Intelligent Systems Reference Library)

This brief monograph is the first one to deal exclusively with the quantitative approximation by artificial neural networks to the identity-unit operator. Here we study with rates the approximation properties of the "right" sigmoidal and hyperbolic tangent artificial neural network positive linear operators. In particular we study...

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...
Data Analysis Using SAS Enterprise Guide
Data Analysis Using SAS Enterprise Guide

The present book, Data Analysis Using SAS Enterprise Guide, provides readers with an overview of Enterprise Guide, the newest point-and-click interface from SAS. SAS Enterprise Guide is a graphical user (point-and-click) interface to the main SAS application, having relatively recently replaced the Analyst interface, which itself had replaced...

S+Functional Data Analysis User's Guide
S+Functional Data Analysis User's Guide
S+Functional Data Analysis is the first commercial object oriented package for exploring, modeling, and analyzing functional data. Functional data analysis (FDA) handles longitudinal data and treats each observation as a function of time (or other variable). The functions are related. The goal is to analyze a sample of functions instead of a...
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