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道路工程数据分析原理与方法

道路工程数据分析原理与方法

出版社:东南大学出版社出版时间:2023-05-01
开本: 24cm 页数: 12,365页
本类榜单:工业技术销量榜
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道路工程数据分析原理与方法 版权信息

道路工程数据分析原理与方法 内容简介

本书介绍道路工程领域数据分析原理与方法,包括基本的数据统计描述和显著性检验,针对试验数据的方差分析及试验设计,传统的线性、多项式、非线性、回归分析,针对特殊因变量的逻辑回归、计数数据模型、生存分析、时间序列、随机过程等方法,针对多元数据的主成分分析、因子分析、聚类分析等无监督机器学习方法,人工智能中常用的决策树、支持向量机、神经网络、判别分析等有监督机器学习方法,以及结构方程模型、马尔科夫蒙特卡洛抽样方法等方法。

道路工程数据分析原理与方法 目录

1 Pavement Performance Data Abstract 1.1 Introduction 1.2 Pavement Performance Indices 1.2.1 Development of Pavement Performance Indices 1.2.2 Pavement Performance Indices in China 1.3 Pavement Management System 1.4 Pavement Performance Models 1.4.1 Classic Pavement Performance Models 1.4.2 Time-Performance Models 1.5 The LTPP Database 1.5.1 The LTPP program 1.5.2 Asphalt Pavement Performance Data in LTPP 1.6 Data Analysis in Pavement Engineering 1.6.1 An Overview 1.6.2 Machine Learning Methods 1.6.3 Summary Questions References 2 Fundamentals of Statistics Abstract 2.1 Introduction 2.2 Random Variables 2.2.1 Discrete Random Variables 2.2.2 Continuous Random Variables 2.2.3 Joint Distribution 2.3 Statistical Descriptions of Data 2.3.1 Sampling Methods 2.3.2 Numerical Summaries 2.3.3 Graphical Summaries 2.3.4 Covariance and Correlation 2.4 Functions of Normal Distributions 2.4.1 Distributions of the Sample Mean and Variance 2.4.2 Comparison of Two Sample Means and Variance 2.5 Statistical Inference 2.5.1 Point Estimate 2.5.2 Interval Estimate 2.6 Hypothesis Tests 2.6.1 Concepts and Procedures 2.6.2 One-Tailed and Two-Tailed Tests 2.6.3 Tests for One Sample 2.6.4 Tests for Two Samples 2.6.5 Proportion Tests 2.7 Case : Significance Test of Concrete Strength 2.7.1 Background and Data 2.7.2 Discussion of Results Questions References 3 Design of Experiments Abstract 3.1 Introduction 3.1.1 Design of Experiments 3.1.2 Analysis of Variance 3.2 Design of Experiments 3.2.1 Definition 3.2.2 Principles 3.2.3 Types of Experimental Designs …… 4 Regression 5 Logistic Regression 6 Count Data Model 7 Survival Analysis 8 Time Series 9 Stochastic Process 10 Decision Trees and Ensemble Learning 11 Neural Networks 12 Support Vector Machine and k-Nearest Neighbors 13 Principal Component Analysis 14 Factor Analysis 15 Cluster Analysis 16 Discriminant Analysis 17 Structural Equation Model 18 Markov Chain Monte Carlo About the Author
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