| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNISA996387168003316 |
|
|
Autore |
Waller William, Sir, <1597?-1668.> |
|
|
Titolo |
A full relation of the late proceedings, victory and good success (through Gods providence) obtained by the Parliaments forces under Sir William Waller, at the taking of the town and castle of Arundell, in Sussex, Decem. 20. and Jan. 6 [[electronic resource] ] : Where were taken above a thousand prisoners, two thousand arms, neer two hundred horse, about a hundred commanders and officers, with great store of treasure. As it was delivered by a messenger from Sir William Waller, to the Right Honorable, William Lenthall Esq; Speaker to the House of Commons. And by him appointed to be forthwith printed and published |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
[London], : Printed by John Field, Jan. 8. 1644 |
|
|
|
|
|
|
|
Descrizione fisica |
|
|
|
|
|
|
Soggetti |
|
Arundel Castle (West Sussex) Siege, 1644 Early works to 1800 |
Great Britain History Civil War, 1642-1649 Campaigns Early works to 1800 |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Place of publication from Wing. |
Reproduction of the original in the British Library. |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
|
|
|
|
|
|
|
|
|
|
|
|
2. |
Record Nr. |
UNINA9910583088403321 |
|
|
Autore |
Sugiyama Masashi <1974-> |
|
|
Titolo |
Introduction to statistical machine learning / / Masashi Sugiyama |
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Amsterdam : , : Elsevier, , [2016] |
|
©2016 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (535 p.) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Machine learning - Statistical methods |
Information science - Statistical methods |
Pattern recognition systems |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Description based upon print version of record. |
|
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references and index. |
|
|
|
|
|
|
Nota di contenuto |
|
Front Cover; Introduction to Statistical Machine Learning; Copyright; Table of Contents; Biography; Preface; 1 INTRODUCTION; 1 Statistical Machine Learning; 1.1 Types of Learning; 1.2 Examples of Machine Learning Tasks; 1.2.1 Supervised Learning; 1.2.2 Unsupervised Learning; 1.2.3 Further Topics; 1.3 Structure of This Textbook; 2 STATISTICS AND PROBABILITY; 2 Random Variables and Probability Distributions; 2.1 Mathematical Preliminaries; 2.2 Probability; 2.3 Random Variable and Probability Distribution; 2.4 Properties of Probability Distributions; 2.4.1 Expectation, Median, and Mode |
2.4.2 Variance and Standard Deviation2.4.3 Skewness, Kurtosis, and Moments; 2.5 Transformation of Random Variables; 3 Examples of Discrete Probability Distributions; 3.1 Discrete Uniform Distribution; 3.2 Binomial Distribution; 3.3 Hypergeometric Distribution; 3.4 Poisson Distribution; 3.5 Negative Binomial Distribution; 3.6 Geometric Distribution; 4 Examples of Continuous Probability Distributions; 4.1 Continuous Uniform Distribution; 4.2 Normal Distribution; 4.3 Gamma Distribution, Exponential Distribution, and Chi-Squared Distribution; 4.4 Beta Distribution |
4.5 Cauchy Distribution and Laplace Distribution4.6 t-Distribution and F-Distribution; 5 Multidimensional Probability Distributions; 5.1 Joint Probability Distribution; 5.2 Conditional Probability Distribution; 5.3 |
|
|
|
|
|
|
|
|
|
|
Contingency Table; 5.4 Bayes' Theorem; 5.5 Covariance and Correlation; 5.6 Independence; 6 Examples of Multidimensional Probability Distributions; 6.1 Multinomial Distribution; 6.2 Multivariate Normal Distribution; 6.3 Dirichlet Distribution; 6.4 Wishart Distribution; 7 Sum of Independent Random Variables; 7.1 Convolution; 7.2 Reproductive Property; 7.3 Law of Large Numbers |
7.4 Central Limit Theorem8 Probability Inequalities; 8.1 Union Bound; 8.2 Inequalities for Probabilities; 8.2.1 Markov's Inequality and Chernoff's Inequality; 8.2.2 Cantelli's Inequality and Chebyshev's Inequality; 8.3 Inequalities for Expectation; 8.3.1 Jensen's Inequality; 8.3.2 Hölder's Inequality and Schwarz's Inequality; 8.3.3 Minkowski's Inequality; 8.3.4 Kantorovich's Inequality; 8.4 Inequalities for the Sum of Independent Random Variables; 8.4.1 Chebyshev's Inequality and Chernoff's Inequality; 8.4.2 Hoeffding's Inequality and Bernstein's Inequality; 8.4.3 Bennett's Inequality |
9 Statistical Estimation9.1 Fundamentals of Statistical Estimation; 9.2 Point Estimation; 9.2.1 Parametric Density Estimation; 9.2.2 Nonparametric Density Estimation; 9.2.3 Regression and Classification; 9.2.4 Model Selection; 9.3 Interval Estimation; 9.3.1 Interval Estimation for Expectation of Normal Samples; 9.3.2 Bootstrap Confidence Interval; 9.3.3 Bayesian Credible Interval; 10 Hypothesis Testing; 10.1 Fundamentals of Hypothesis Testing; 10.2 Test for Expectation of Normal Samples; 10.3 Neyman-Pearson Lemma; 10.4 Test for Contingency Tables |
10.5 Test for Difference in Expectations of Normal Samples |
|
|
|
|
|
| |