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1.

図書
図書
1. The logic of conditionals : an application of probability to deductive logic
Ernest W. Adams
出版情報: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1975  xiii, 155 p. ; 23 cm
シリーズ名: Synthese library ; v. 86
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2.

図書
図書
2. Statistics
Narayan C. Giri
出版情報: New York : M. Dekker, c1975  viii, 314 p. ; 24 cm
シリーズ名: Statistics : textbooks and monographs ; v. 7 . Introduction to probability and statistics ; pt. 2
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3.

図書
図書
3. Transform techniques for probability modeling
Walter C. Giffin
出版情報: New York : Academic Press, 1975  xiii, 233 p. ; 24 cm
シリーズ名: Operations research and industrial engineering
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4.

図書
図書
4. Decomposition of multivariate probabilities
Roger Cuppens
出版情報: New York : Academic Press, 1975  xv, 244 p. ; 24 cm
シリーズ名: Probability and mathematical statistics : a series of monographs and textbooks ; v. 29
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5.

図書
図書
5. 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：
6.

図書
図書
6. Probability and statistics
Morris H. DeGroot
出版情報: Reading, Mass. : Addison-Wesley, c1975  xiv, 607 p. ; 25 cm
シリーズ名: Addison-Wesley series in behavioral science : quantitative methods
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目次情報: 続きを見る
Introduction to Probability / 1：
The History of Probability
Interpretations of Probability
Experiments and Events
Set Theory
The Definition of Probability
Finite Sample Spaces
Counting Methods
Combinatorial Methods
Multinomial Coefficients
The Probability of a Union of Events
Statistical Swindles
Supplementary Exercises
Conditional Probability / 2：
The Definition of Conditional Probability
Independent Events
Bayes' Theorem
Markov Chains
The Gambler's Ruin Problem
Random Variables and Distribution / 3：
Random Variables and Discrete Distributions
Continuous Distributions
The Distribution Function
Bivariate Distributions
Marginal Distributions
Conditional Distributions
Multivariate Distributions
Functions of a Random Variable
Functions of Two or More Random Variables
Expectation / 4：
The Expectation of a Random Variable
Properties of Expectations
Variance
Moments
The Mean and The Median
Covariance and Correlation
Conditional Expectation
The Sample Mean
Utility
Special Distributions / 5：
Introduction
The Bernoulli and Binomial Distributions
The Hypergeometric Distribution
The Poisson Distribution
The Negative Binomial Distribution
The Normal Distribution
The Central Limit Theorem
The Correction for Continuity
The Gamma Distribution
The Beta Distribution
The Multinomial Distribution
The Bivariate Normal Distribution
Estimation / 6：
Statistical Inference
Prior and Posterior Distributions
Conjugate Prior Distributions
Bayes Estimators
Maximum Likelihood Estimators
Properties of Maximum Likelihood Estimators
Sufficient Statistics
Jointly Sufficient Statistics
Improving an Estimator
Sampling Distributions of Estimators / 7：
The Sampling Distribution of a Statistic
The Chi-Square Distribution
Joint Distribution of the Sample Mean and Sample Variance
The t Distribution
Confidence Intervals
Bayesian Analysis of Samples from a Normal Distribution
Unbiased Estimators
Fisher Information
Testing Hypotheses / 8：
Problems of Testing Hypotheses
Testing Simple Hypotheses
Uniformly Most Powerful Tests
Two-Sided Alternatives
The t Test
Comparing the Means of Two Normal Distributions
The F Distribution
Bayes Test Procedures
Foundational Issues
Categorical Data and Nonparametric Methods / 9：
Tests of Goodness-of-Fit
Goodness-of-Fit for Composite Hypotheses
Contingency Tables
Tests of Homogeneit
Simpson's Paradox
Kolmogorov-Smirnov Test
Robust Estimation
Sign and Rank Tests
Linear Statistical Models / 10：
The Method of Least Squares
Regression
Statistical Inference in Simple Linear Regression
Bayesian Inference in Simple Linear Regression
The General Linear Model and Multiple Regression
Analysis of Variance
The Two-Way Layout
The Two-Way Layout with Replications
Simulation / 11：
Why is Simulation Useful?
Simulating Specific Distributions
Importance Sampling
Markov Chain Monte Carlo
The Bootstrap
Introduction to Probability / 1：
The History of Probability
Interpretations of Probability
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