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道路工程數(shù)據(jù)分析原理與方法

道路工程數(shù)據(jù)分析原理與方法

出版社:東南大學出版社出版時間:2023-05-01
開本: 24cm 頁數(shù): 12,365頁
中 圖 價:¥75.5(7.7折) 定價  ¥98.0 登錄后可看到會員價
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道路工程數(shù)據(jù)分析原理與方法 版權(quán)信息

道路工程數(shù)據(jù)分析原理與方法 內(nèi)容簡介

本書介紹道路工程領(lǐng)域數(shù)據(jù)分析原理與方法,包括基本的數(shù)據(jù)統(tǒng)計描述和顯著性檢驗,針對試驗數(shù)據(jù)的方差分析及試驗設(shè)計,傳統(tǒng)的線性、多項式、非線性、回歸分析,針對特殊因變量的邏輯回歸、計數(shù)數(shù)據(jù)模型、生存分析、時間序列、隨機過程等方法,針對多元數(shù)據(jù)的主成分分析、因子分析、聚類分析等無監(jiān)督機器學習方法,人工智能中常用的決策樹、支持向量機、神經(jīng)網(wǎng)絡、判別分析等有監(jiān)督機器學習方法,以及結(jié)構(gòu)方程模型、馬爾科夫蒙特卡洛抽樣方法等方法。

道路工程數(shù)據(jù)分析原理與方法 目錄

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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