Introduction to Probability and Statistics from a Bayesian Viewpoint, Part 2, Inference
By (author) D. V. Lindley
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Introduction to Probability and Statistics from a Bayesian Viewpoint, Part 2, Inference by D. V. Lindley
Book DescriptionThe two parts of this book treat probability and statistics as mathematical disciplines and with the same degree of rigour as is adopted for other branches of applied mathematics at the level of a British honours degree. They contain the minimum information about these subjects that any honours graduate in mathematics ought to know. They are written primarily for general mathematicians, rather than for statistical specialists or for natural scientists who need to use statistics in their work. No previous knowledge of probability or statistics is assumed, though familiarity with calculus and linear algebra is required. The first volume takes the theory of probability sufficiently far to be able to discuss the simpler random processes, for example, queueing theory and random walks. The second volume deals with statistics, the theory of making valid inferences from experimental data, and includes an account of the methods of least squares and maximum likelihood; it uses the results of the first volume.
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Book DetailsISBN: 9780521298667
(216mm x 138mm x 18mm)
Imprint: Cambridge University Press
Publisher: Cambridge University Press
Publish Date: 20-Mar-1980
Country of Publication: United Kingdom
Books By Author D. V. Lindley
Making Decisions, Paperback (October 1985)
Making Decisions Second Edition D.V. Lindley Formerly Professor of Statistics, University College London This book looks at the problems involved in decision-making and argues that there is only one logical way to make a decision.
Introduction to Probability and Statistics from a Bayesian Viewpoint, Part 1, Probability, Paperback (March 1980)
The two parts of this book treat probability and statistics as mathematical disciplines.
Bayesian Statistics, Paperback (January 1972)» View all books by D. V. Lindley
A study of those statistical ideas that use a probability distribution over parameter space.
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