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Yong Chen
Industrial Data Analytics for Diagnosis and Prognosis
Читать книгу Industrial Data Analytics for Diagnosis and Prognosis - Yong Chen - Страница 1
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Страница 1
Industrial Data Analytics for Diagnosis and Prognosis A Random Effects Modelling Approach
Страница 3
Страница 4
Contents
Guide
Pages
Preface
Acknowledgments
Table of Notation
1 Introduction 1.1 Background and Motivation
1.2 Scope and Organization of the Book
1.3 How to Use This Book
Bibliographic Notes
Страница 15
2 Introduction to Data Visualization and Characterization
2.1 Data Visualization
2.1.1 Distribution Plots for a Single Variable
Distribution of A Categorical Variable – Bar Chart
Distribution of Numerical Variables – Histogram and Box Plot
2.1.2 Plots for Relationship Between Two Variables
Relationship Between Two Numerical Variables – Scatter Plot
Relationship Between A Numerical Variable and A Categorical Variable – Side-by-Side Box Plot
Relationship Between Two Categorical Variables – Mosaic Plot
2.1.3 Plots for More than Two Variables
Color Coded Scatter Plot
Scatter Plot Matrix and Heatmap
2.2 Summary Statistics
2.2.1 Sample Mean, Variance, and Covariance Sample Mean – Measure of Location
Sample Variance – Measure of Spread
Sample Covariance and Correlation – Measure of Linear Association Between Two Variables
2.2.2 Sample Mean Vector and Sample Covariance Matrix
2.2.3 Linear Combination of Variables
Bibliographic Notes
Exercises
3 Random Vectors and the Multivariate Normal Distribution
3.1 Random Vectors
3.2 Density Function and Properties of Multivariate Normal Distribution
Properties of the Multivariate Normal Distribution
3.3 Maximum Likelihood Estimation for Multivariate Normal Distributions
3.4 Hypothesis Testing on Mean Vectors
3.5 Bayesian Inference for Normal Distribution
Bibliographic Notes
Exercises
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