Statistical Foundations, Reasoning and Inference
For Science and Data Science
Series: Springer Series in Statistics;
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35 498 Ft
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Product details:
- Edition number 1st ed. 2021
- Publisher Springer International Publishing
- Date of Publication 2 October 2022
- Number of Volumes 1 pieces, Book
- ISBN 9783030698294
- Binding Paperback
- See also 9783030698263
- No. of pages356 pages
- Size 235x155 mm
- Weight 569 g
- Language English
- Illustrations XIII, 356 p. 87 illus., 10 illus. in color. Illustrations, black & white 292
Categories
Long description:
This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master’s students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.
MoreTable of Contents:
Introduction.- Background in Probability.- Parametric Statistical Models.- Maximum Likelihood Inference.- Bayesian Statistics.- Statistical Decisions.- Regression.- Bootstrapping.- Model Selection and Model Averaging.- Multivariate and Extreme Value Distributions.- Missing and Deficient Data.- Experiments and Causality.
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