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1.

図書
図書
1. Applied statistics and probability for engineers. 6th edition., International student version (: pbk)
Douglas C. Montgomery, George C. Runger
出版情報: Hoboken, N.J. : J. Wiley, c2014  xvi, 765 p. ; 26 cm
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2.

図書
図書
2. Whys and hows in uncertainty modelling : probability, fuzziness and anti-optimization
edited by Isaac Elishakoff
出版情報: Wien : Springer-Verlag, c1999  393 p. ; 24 cm
シリーズ名: CISM courses and lectures ; no. 388
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3.

図書
図書
3. Probability and statistical inference for scientists and engineers
[by] Isaac N. Gibra
出版情報: Englewood Cliffs, N.J. : Prentice-Hall, [1973]  xii, 596 p. ; 24 cm
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4.

図書
図書
4. Random noise, signals, and dynamic systems
Larson, Harold J., 1934- ; Shubert, Bruno O.
出版情報: New York : Wiley, c1979  737 p. ; 24 cm
シリーズ名: Probabilistic models in engineering sciences / Harold J. Larson, Bruno O. Scubert ; v. 2
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5.

図書
図書
5. Random variables and stochastic processes
Harold J. Larson, Bruno O. Shubert
出版情報: New York : Wiley, c1979  x, 544 p. ; 24 cm
シリーズ名: Probabilistic models in engineering sciences / Harold J. Larson, Bruno O. Scubert ; v. 1
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6.

図書
図書
6. Probabilistic models in engineering sciences
Harold J. Larson, Bruno O. Scubert
出版情報: New York : Wiley, c1979  2 v. ; 24 cm
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7.

図書
図書
7. Introduction to probability theory and stochastic processes (: [hardcover])
John Chiasson
出版情報: Hoboken, N.J. : John Wiley & Sons, c2013  xxii, 959 p. ; 25 cm
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8.

図書
図書
8. Basic principles (: pbk)
Alfredo H-S. Ang, Wilson H. Tang
出版情報: New York : Wiley, 1975  xiii, 409 p. ; 24 cm
シリーズ名: Probability concepts in engineering planning and design ; v. 1
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目次情報: 続きを見る
Role of Probability in Engineering / 1：
Introduction / 1.1：
Uncertainty in Real-World Information / 1.2：
Uncertainty Associated with Randomness / 1.2.1：
Uncertainty Associated with Imperfect Modeling and Estimation / 1.2.2：
Design and Decision-Making Under Uncertainty / 1.3：
Planning and Design of Airport Pavement / 1.3.1：
Hydrologic Design / 1.3.2：
Design of Structures and Machines / 1.3.3：
Geotechnical Design / 1.3.4：
Construction Planning and Management / 1.3.5：
Photogrammetric, Geodetic, and Surveying Measurements / 1.3.6：
Control and Standards / 1.4：
Concluding Remarks / 1.5：
Basic Probability Concepts / 2：
Events and Probability / 2.1：
Characteristics of Probability Problems / 2.1.1：
Calculation of Probability / 2.1.2：
Elements of Set Theory / 2.2：
Definitions / 2.2.1：
Combination of Events / 2.2.2：
Operational Rules / 2.2.3：
Mathematics of Probability / 2.3：
Basic Axioms of Probability Addition Rule / 2.3.1：
Conditional Probability Multiplication Rule / 2.3.2：
Theorem of Total Probability / 2.3.3：
Bayes' Theorem / 2.3.4：
Concluding Remarks Problems / 2.4：
Analytical Models of Random Phenomena / 3：
Random Variables / 3.1：
Probability Distribution of a Random Variable / 3.1.1：
Main Descriptors of a Random Variable / 3.1.2：
Useful Probability Distributions / 3.2：
The Normal Distribution / 3.2.1：
The Logarithmic Normal Distribution / 3.2.2：
Bernoulli Sequence and the Binomial Distribution / 3.2.3：
The Geometric Distribution / 3.2.4：
The Negative Binomial Distribution / 3.2.5：
The Poisson Process and Poisson Distribution / 3.2.6：
The Exponential Distribution / 3.2.7：
The Gamma Distribution / 3.2.8：
The Hypergeometric Distribution / 3.2.9：
The Beta Distribution / 3.2.10：
Other Distributions / 3.2.11：
Multiple Random Variables / 3.3：
Joint and Conditional Probability Distributions / 3.3.1：
Covariance and Correlation / 3.3.2：
Conditional Mean and Variance / 3.3.3：
Functions of Random Variables / 3.4：
Derived Probability Distributions / 4.1：
Function of Single Random Variable / 4.2.1：
Function of Multiple Random Variables / 4.2.2：
Moments of Functions of Random Variables / 4.3：
Mean and Variance of a Linear Function / 4.3.1：
Product of Independent Variates / 4.3.3：
Mean and Variance of a General Function / 4.3.4：
Estimating Parameters from Observational Data / 4.4：
The Role of Statistical Inference in Engineering / 5.1：
Inherent Variability and Estimation Error / 5.1.1：
Classical Approach to Estimation of Parameters / 5.2：
Random Sampling and Point Estimation / 5.2.1：
Interval Estimation of the Mean / 5.2.2：
Problems of Measurement Theory / 5.2.3：
Interval Estimation of the Variance / 5.2.4：
Estimation of Proportion / 5.2.5：
Empirical Determination of Distribution Models / 5.3：
Probability Paper / 6.1：
The Normal Probability Paper / 6.2.1：
The Log-Normal Probability Paper / 6.2.2：
Construction of General Probability Paper / 6.2.3：
Testing Validity of Assumed Distribution / 6.3：
Chi-Square Test for Distribution / 6.3.1：
Kolmogorov-Smirnov Test for Distribution / 6.3.2：
Regression and Correlation Analyses / 6.4：
Basic Formulation of Linear Regression / 7.1：
Regression with Constant Variance / 7.1.1：
Regression with Nonconstant Variance / 7.1.2：
Multiple Linear Regression / 7.2：
Nonlinear Regression / 7.3：
Applications of Regression Analysis in Engineering / 7.4：
Correlation Analysis / 7.5：
Estimation of Correlation Coefficient / 7.5.1：
The Bayesian Approach / 7.6：
Basic Concepts-The Discrete Case / 8.1：
The Continuous Case / 8.3：
General Formulation / 8.3.1：
A Special Application of Bayesian Up-dating Process / 8.3.2：
Bayesian Concepts in Sampling Theory / 8.4：
Sampling from Normal Population / 8.4.1：
Error in Estimation / 8.4.3：
Use of Conjugate Distributions / 8.4.4：
Elements of Quality Assurance and Acceptance Sampling / 8.5：
Acceptance Sampling by Attributes / 9.1：
The Operating Characteristic (OC) Curve / 9.1.1：
The Success Run / 9.1.2：
The Average Outgoing Quality Curve / 9.1.3：
Acceptance Sampling by Variables / 9.2：
Average Quality Criterion, sigma Known / 9.2.1：
Average Quality Criterion, sigma Unknown / 9.2.2：
Fraction Defective Criterion / 9.2.3：
Multiple-Stage Sampling / 9.3：
Probability Tables / 9.4：
Table of Standard Normal Probability / Table A.1：
p-Percentile Values of the t-Distribution / Table A.2：
p-Percentile Values of the x 2 -Distribution / Table A.3：
Critical Values of D alpha; in the Kolmogorov-Smirnov Test / Table A.4：
Combinatorial Formulas / Appendix B：
Derivation of the Poisson Distribution / Appendix C：
References
Index
Role of Probability in Engineering / 1：
Introduction / 1.1：
Uncertainty in Real-World Information / 1.2：
9.

図書
図書
9. Probability concepts in engineering : emphasis on applications in civil and environmental engineering. 2nd ed
Alfredo H-S. Ang, Wilson H. Tang
出版情報: New York : Wiley, c2007  xiii, 406 p. ; 27 cm
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目次情報: 続きを見る
Preface
Roles of Probability and Statistics in Engineering / Chapter 1：
Introduction / 1.1：
Uncertainty in Engineering / 1.2：
Uncertainty Associated with Randomness-The Aleatory Uncertainty / 1.2.1：
Uncertainty Associated with Imperfect Knowledge-The Epistemic Uncertainty / 1.2.2：
Design and Decision Making under Uncertainty / 1.3：
Planning and Design of Transportation Infrastructures / 1.3.1：
Design of Structures and Machines / 1.3.2：
Planning and Design of Hydrosystems / 1.3.3：
Design of Geotechnical Systems / 1.3.4：
Construction Planning and Management / 1.3.5：
Photogrammetric, Geodetic, and Surveying Measurements / 1.3.6：
Applications in Quality Control and Assurance / 1.3.7：
Concluding Summary / 1.4：
References
Fundamentals of Probability Models / Chapter 2：
Events and Probability / 2.1：
Characteristics of Problems Involving Probabilities / 2.1.1：
Estimating Probabilities / 2.1.2：
Elements of Set Theory-Tools for Defining Events / 2.2：
Important Definitions / 2.2.1：
Mathematical Operations of Sets / 2.2.2：
Mathematics of Probability / 2.3：
The Addition Rule / 2.3.1：
Conditional Probability / 2.3.2：
The Multiplication Rule / 2.3.3：
The Theorem of Total Probability / 2.3.4：
The Bayes' Theorem / 2.3.5：
Problems / 2.4：
Analytical Models of Random Phenomena / Chapter 3：
Random Variables and Probability Distribution / 3.1：
Random Events and Random Variables / 3.1.1：
Probability Distribution of a Random Variable / 3.1.2：
Main Descriptors of a Random Variable / 3.1.3：
Useful Probability Distributions / 3.2：
The Gaussian (or Normal) Distribution / 3.2.1：
The Lognormal Distribution / 3.2.2：
The Bernoulli Sequence and the Binomial Distribution / 3.2.3：
The Geometric Distribution / 3.2.4：
The Negative Binomial Distribution / 3.2.5：
The Poisson Process and the Poisson Distribution / 3.2.6：
The Exponential Distribution / 3.2.7：
The Gamma Distribution / 3.2.8：
The Hypergeometric Distribution / 3.2.9：
The Beta Distribution / 3.2.10：
Other Useful Distributions / 3.2.11：
Multiple Random Variables / 3.3：
Joint and Conditional Probability Distributions / 3.3.1：
Covariance and Correlation / 3.3.2：
Functions of Random Variables / 3.4：
Derived Probability Distributions / 4.1：
Function of a Single Random Variable / 4.2.1：
Function of Multiple Random Variables / 4.2.2：
Extreme Value Distributions / 4.2.3：
Moments of Functions of Random Variables / 4.3：
Mathematical Expectations of a Function / 4.3.1：
Mean and Variance of a General Function / 4.3.2：
Computer-Based Numerical and Simulation Methods in Probability / 4.4：
Numerical and Simulations Methods / 5.1：
Essentials of Monte Carlo Simulation / 5.2.1：
Numerical Examples / 5.2.2：
Problems Involving Aleatory and Epistemic Uncertainties / 5.2.3：
MCS Involving Correlated Random Variables / 5.2.4：
References and Softwares / 5.3：
Statistical Inferences from Observational Data / Chapter 6：
Role of Statistical Inference in Engineering / 6.1：
Statistical Estimation of Parameters / 6.2：
Random Sampling and Point Estimation / 6.2.1：
Sampling Distributions / 6.2.2：
Testing of Hypotheses / 6.3：
Hypothesis Test Procedure / 6.3.1：
Confidence Intervals / 6.4：
Confidence Interval of the Mean / 6.4.1：
Confidence Interval of the Proportion / 6.4.2：
Confidence Interval of the Variance / 6.4.3：
Measurement Theory / 6.5：
Determination of Probability Distribution Models / 6.6：
Probability Papers / 7.1：
Utility and Plotting Position / 7.2.1：
The Normal Probability Paper / 7.2.2：
The Lognormal Probability Paper / 7.2.3：
Construction of General Probability Papers / 7.2.4：
Testing Goodness-of-Fit of Distribution Models / 7.3：
The Chi-Square Test for Goodness-of-Fit / 7.3.1：
The Kolmogorov-Smirnov (K-S) Test for Goodness-of-Fit / 7.3.2：
The Anderson-Darling Test for Goodness-of-Fit / 7.3.3：
Invariance in the Asymptotic Forms of Extremal Distributions / 7.4：
Regression and Correlation Analyses / 7.5：
Fundamentals of Linear Regression Analysis / 8.1：
Regression with Constant Variance / 8.2.1：
Variance in Regression Analysis / 8.2.2：
Confidence Intervals in Regression / 8.2.3：
Correlation Analysis / 8.3：
Estimation of the Correlation Coefficient / 8.3.1：
Regression of Normal Variates / 8.3.2：
Linear Regression with Nonconstant Variance / 8.4：
Multiple Linear Regression / 8.5：
Nonlinear Regression / 8.6：
Applications of Regression Analysis in Engineering / 8.7：
The Bayesian Approach / 8.8：
Estimation of Parameters / 9.1：
Basic Concepts-The Discrete Case / 9.2：
The Continuous Case / 9.3：
General Formulation / 9.3.1：
A Special Application of the Bayesian Updating Process / 9.3.2：
Bayesian Concept in Sampling Theory / 9.4：
Sampling from Normal Populations / 9.4.1：
Error in Estimation / 9.4.3：
The Utility of Conjugate Distributions / 9.4.4：
Estimation of Two Parameters / 9.5：
Bayesian Regression and Correlation Analyses / 9.6：
Linear Regression / 9.6.1：
Updating the Regression Parameters / 9.6.2：
Elements of Quality Assurance and Acceptance Sampling / 9.6.3：
Appendices
Probability Tables / Appendix A：
Standard Normal Probabilities / Table A.1：
CDF of the Binomial Distribution / Table A.2：
Critical Values of t-Distribution at Confidence Level (1-[alpha]) = p / Table A.3：
Critical Values of the x[superscript 2] Distribution at probability Level [alpha] / Table A.4：
Critical Values of D[superscript alpha subscript n] at Significance Level [alpha] in the K-S Test / Table A.5：
Critical Values of the Anderson-Darling Goodness-of-Fit Test / Table A.6：
Combinatorial Formulas / Appendix B：
The Basic Relation / B.1：
The Binomial Coefficient / B.3：
The Multinomial Coefficient / B.4：
Stirling's Formula / B.5：
Derivation of the Poisson Distribution / Appendix C：
Index
Preface
Roles of Probability and Statistics in Engineering / Chapter 1：
Introduction / 1.1：
10.

図書
図書
10. Applied probability and stochastic processes in engineering and physical sciences
Michel K. Ochi
出版情報: New York : Wiley, c1990  xvi, 499 p. ; 24 cm
シリーズ名: Wiley series in probability and mathematical statistics ; . Applied probability and statistics
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目次情報: 続きを見る
Elements of Probability
Random Variables and Their Probability Distributions
Moments of Random Variables
Moment Generating Function, Characteristic Function, and Their Application
Discrete Random Variables and Their Distributions
Continuous Random Variables and Their Distributions
Transformation of Random Variables
Extreme Value Statistics
Stochastic Processes
Spectral Analysis of Stochastic Processes
Amplitudes and Periods of Gaussian Random Processes
Statistical Analysis of Time Series Data
Wiener-LTvy and Markov Processes
Linear System and Stochastic Prediction
Nonlinear Systems and Stochastic Prediction
Non-Gaussian Stochastic Processes
Counting Stochastic Processes
Exercises
Appendices
References
Index
Elements of Probability
Random Variables and Their Probability Distributions
Moments of Random Variables
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