Thanks to a generous grant from the GRAMMY Foundation, the UCSB Library is pleased to announce an additional 1,000 cylinders have been digitized and made accessible online. A listing of cylinders digitized to date is below. Most of the document details honours & awards, mostly British units but there are two mentions of Australian awards of the Military Medal to-1298 A/Bdr JM Mackie Aust DAC & 0059 Sgt W Fleming Aust DAC plus other items mentioned. Produced for Brig A J.
A history of mathematical statistics from 1. Hald. A history of mathematical statistics from 1. ISBN 0. 47. 11. 79. Plan of the Book 1.
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Denne side er blot en simpel liste med de b Zine devoted to the arts and urban affairs with emphasis on New York City, architecture, museums, art auctions, art attributions, landmarks, Upper East Side, Midtown, Upper West Side, Chelsea, Sutton Place, photography, computer art, poetry and film. Cornell University was founded on April 27, 1865; the New York State (NYS) Senate authorized the university as the state's land grant institution. Senator Ezra Cornell offered his farm in Ithaca, New York as a site and $500,000 of his personal fortune as an initial endowment. HALD, Anders A history of mathematical statistics from 1750 to 1930 1998 ISBN 0471179124 Contents Preface XV 1. Plan of the Book 1 1.1. Outline of the Contents 1 1.2. Terminology and Notation 7 1.3. Biographies 8 PART I DIRECT PROBABILITY, 1750-1805 2.
Outline of the Contents. Terminology and Notation 7. Biographies 8. PART I DIRECT PROBABILITY, 1.
Some Results and Tools in Probability Theory by Bernoulli, de Moivre. The Discrete Equiprobability Model 1. The Theorems of James and Nicholas Bernoulli, 1. The Normal Distribution as Approximation to the Binomial.
De Moivre's. Theorem, 1. Its Modifications by Lagrange, 1. Laplace, 1. 81. 2. Laplace's Analytical Probability Theory 2.
The Distribution of the Arithmetic Mean, 1. The Measurement Error Model 3. The Distribution of the Sum of the Number of Points by n Throws. Die by Montmort and de Moivre 3. The Mean of Triangularly Distributed Errors. Simpson, 1. 75. 6- 1. The Mean of Multinomially and Continuously Distributed Errors.
Asymptotic Normality of the Multinomial. Lagrange, 1. 77. 6 4. The Mean of Continuous Rectangularly Distributed Observations.
Laplace's Convolution Formula for the Distribution of a Sum, 1. Tests of Significance 6.
Moral Impossibility and Statistical Significance 6. Daniel Bernoulli's Test for the Random Distribution of the Inclinations. Planetary Orbits, 1. John Michell's Test for the Random Distribution of the Positions. Fixed Stars, 1. 76. Laplace's Test of Significance for the Mean Inclination, 1.
Theory of Errors and Methods of Estimation 7. Theory of Errors and the Method of Maximum Likelihood by Lambert. Theory of Errors and the Method of Maximum Likelihood by Daniel. Bernoulli, 1. 77.
Methods of Estimation by Laplace before 1. Fitting of Equations to Data, 1. The Multiparameter Measurement Error Model 9.
The Method of Averages by Tobias Mayer, 1. The Method of Least Absolute Deviations by Boscovich, 1. Numerical and Graphical Curve Fitting by Lambert, 1. Laplace's Generalization of Mayer's Method, 1.
Minimizing the Largest Absolute Residual. Laplace, 1. 78. 6, 1. Laplace's Modification of Boscovich's Method, 1. Laplace's Determination of the Standard Meter, 1. Legendre's Method of Least Squares, 1.
PART II INVERSE PROBABILITY BY BAYES AND LAPLACE, WITH COMMENTS ON. LATER DEVELOPMENTS.
Induction and Probability: The Philosophical Background 1. Newton's Inductive- Deductive Method 1. Hume's Ideas on Induction and Probability, 1. Hartley on Direct and Inverse Probability, 1. Bayes, Price, and the Essay, 1. Lives of Bayes and Price 1.
Bayes's Probability Theory 1. The Posterior Distribution of the Probability of Success 1.
Bayes's Scholium and His Conclusion 1. Price's Commentary 1. Evaluations of the Beta Probability Integral by. Bayes and Price 1. Equiprobability, Equipossibility, and Inverse Probability 1. Bernoulli's Concepts of Probability, 1. Laplace's Definitions of Equiprobability and Equipossibility.
Laplace's Principle of Inverse Probability, 1. Laplace's Proofs of Bayes's Theorem, 1. Laplace's Applications of the Principle of Inverse. Probability in 1. Introduction 1. 67.
Testing a Simple Hypothesis against a Simple Alternative 1. Estimation and Prediction from a Binomial Sample 1. A Principle of Estimation and Its Application to Estimate the. Location Parameter in the Measurement Error Model 1. Laplace's Two Error Distributions 1. The Posterior Median Equals the Arithmetic Mean for a Uniform. Error Distribution, 1.
The Posterior Median for Multinomially Distributed Errors and. Rule of Succession, 1. Laplace's General Theory of Inverse Probability 1. The Memoirs from 1. The Discrete Version of Laplace's Theory 1. The Continuous Version of Laplace's Theory 1. The Equiprobability Model and the Inverse Probability Model for.
Games of Chance 1. Theoretical and Empirical Analyses of Games of Chance 1. The Binomial Case Illustrated by Coin Tossings 1.
A Solution of the Problem of Points for Unknown Probability of. The Multinomial Case Illustrated by Dice Throwing 1. Poisson's Analysis of Buffon's Coin- Tossing Data 1. Pearson and Fisher's Analyses of Weldon's Dice- Throwing Data. Some Modem Uses of the Equiprobability Model 2.
Laplace's Methods of Asymptotic Expansion, 1. Motivation and Some General Remarks 2.
Laplace's Expansions of the Normal Probability Integral 2. The Tail Probability Expansion 2. The Expansion about the Mode 2. Two Related Expansions from the 1.
Expansions of Multiple Integrals 2. Asymptotic Expansion of the Tail Probability of a Discrete Distribution. Laplace Transforms 2. Laplace's Analysis of Binomially Distributed Observations 2. Background for the Problem and the Data 2.
A Test for the Hypothesis 6 . A Test for the Hypothesis 0: < r Against 0 > r Based on the. Normal Probability Expansion, 1. Tests for the Hypothesis 0. Against 0. 2 > 0. Looking for Assignable Causes 2. The Posterior Distribution of 0 Based on Compound Events, 1.
Laplace's Theory of Statistical Prediction 2. The Prediction Formula 2. Predicting the Outcome of a Second Binomial Sample from the Outcome. First 2. 49. 1. 5.
Laplace's Rule of Succession 2. Theory of Prediction for a Finite Population. Prevost and Lhuilier. Laplace's Asymptotic Theory of Statistical Prediction, 1. Notes on the History of the Indifference Principle and the Rule. Succession from Laplace to Jeffreys (1.
Laplace's Sample Survey of the Population of France and the Distribution. Ratio Estimator 2. The Ratio Estimator 2.
Distribution of the Ratio Estimator. Sample Survey of the French Population in 1. From Laplace to Bowley (1. Pearson (1. 92. 8), and Neyman (1. PART III THE NORMAL DISTRIBUTION, THE METHOD OF LEAST SQUARES, AND. THE CENTRAL LIMIT THEOREM. GAUSS AND LAPLACE, 1.
Early History of the Central Limit Theorem, 1. The Characteristic Function and the Inversion Formula for a Discrete.
Distribution by Laplace, 1. Laplace's Central Limit Theorem, 1. Poisson's Proofs, 1.
Bessel's Proof, 1. Cauchy's Proofs, 1. Ellis's Proof, 1. Notes on Later Developments 3. Laplace's Diffusion Model, 1.
Gram- Charlier and Edgeworth Expansions 3. Derivations of the Normal Distribution as a Law of Error 3. Gauss's Derivation of the Normal Distribution and the Method.
Least Squares, 1. Laplace's Large- Sample Justification of the Method of Least Squares. His Criticism of Gauss, 1.
Bessel's Comparison of Empirical Error Distributions with the. Normal Distribution, 1. The Hypothesis of Elementary Errors by Hagen, 1. Bessel. 1. 8. 5. Derivations by Adrain, 1.
Herschel, 1. 85. 0, and Maxwell, 1. Generalizations of Gauss's Proof: The Exponential Family of Distributions.
Notes and References 3. Gauss's Linear Normal Model and the Method of Least Squares, 1. The Linear Normal Model 3. Gauss's Method of Solving the Normal Equations 3. The Posterior Distribution of the Parameters 3.
Gauss's Remarks on Other Methods of Estimation 3. The Priority Dispute between Legendre and Gauss 3. Laplace's Large- Sample Theory of Linear Estimation, 1. Main Ideas in Laplace's Theory of Linear Estimation, 1. The Best Linear Asymptotically Normal Estimate for One Parameter. Asymptotic Normality of Sums of Powers of the Absolute Errors. The Multivariate Normal as the Limiting Distribution of Linear.
Forms of Errors, 1. The Best Linear Asymptotically Normal Estimates for Two Parameters. Laplace's Orthogonalization of the Equations of Condition and. Asymptotic Distribution of the Best Linear Estimates in the Multiparameter. Model, 1. 81. 6 4.
The Posterior Distribution of the Mean and the Squared Precision. Normally Distributed Observations, 1. Application in Geodesy and the Propagation of Error, 1. Linear Estimation with Several Independent Sources of Error. Tides of the Sea and the Atmosphere, 1. Asymptotic. Efficiency of Some Methods of Estimation, 1.
Asymptotic Equivalence of Statistical Inference by. Direct and. Inverse Probability 4. Gauss's Theory of Linear Unbiased Minimum Variance Estimation. Asymptotic Relative Efficiency of Some Estimates of the Standard. Deviation in the Normal Distribution, 1.
Expectation, Variance, and Covariance of Functions of Random. Variables, 1. 82. Gauss's Lower Bound for the Concentration of the Probability. Mass in a Unimodal Distribution, 1.
Gauss's Theory of Linear Minimum Variance Estimation, 1. The Theorem on the Linear Unbiased Minimum Variance Estimate. The Best Estimate of a Linear Function of the Parameters, 1. The Unbiased Estimate of a. Its Variance, 1. 82.
Recursive Updating of the Estimates by an Additional Observation. Estimation under Linear Constraints, 1. A Review 4. 88. PART IV SELECTED TOPICS IN ESTIMATION THEORY, 1. On Error and Estimation Theory, 1. Bibliographies on the Method of Least Squares 4. State of Estimation Theory around 1.
Discussions on the Method of Least Squares and Some Alternatives. The Multivariate Central Limit Theorem, 1. Bravais's Confidence Ellipsoids, 1. Cauchy's Method for Determining the Number of Terms To Be Included. Linear Model and for Estimating the Parameters, 1. The Problem 5. 11.
Solving the Problem by Means of the Instrumental Variable +1. Cauchy's Two- Factor Multiplicative Model, 1. Orthogonalization and Polynomial Regression 5.
Orthogonal Polynomials Derived by Laplacean Orthogonalization. Chebyshev's Orthogonal Polynomials, Least Squares, and Continued.
Fractions, 1. 85. Chebyshev's Orthogonal Polynomials for Equidistant Arguments. Gram's Derivation of Orthogonal Functions by the Method of Least. Squares, 1. 87. 9, 1. Thiele's Free Functions and His Orthogonalization of the Linear. Model, 1. 88. 9, 1. Schmidt's Orthogonalization Process, 1.
Notes on the Literature after 1. Least Squares Approximation.
Orthogonal Polynomials with Equidistant Arguments 5. Statistical Laws in the Social and Biological Sciences. Poisson. Quetelet, and Galton, 1. Probability Theory in the Social Sciences by Condorcet and Laplace. Quetelet on the Average Man, 1. Variation around. Average, 1. 84. 6 5.
Galton on Heredity, Regression, and Correlation, 1. Notes on the Early History of Regression and Correlation, 1.
Sampling Distributions under Normality 6. The Helmert Distribution, 1.
Its Generalization to the. Linear Model by Fisher, 1. The Distribution of the Mean Deviation by Helmert, 1. Fisher, 1. 92. 0 6.