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Weighing the odds: a course in probability and statistics Williams, David

By: Material type: TextTextPublication details: Cambridge University Press 2010 New YorkDescription: xvii, 547 pISBN:
  • 9780521006187
Subject(s): DDC classification:
  • 519.2 W4W3
Summary: Statistics do not lie, nor is probability paradoxical. You just have to have the right intuition. In this lively look at both subjects, David Williams convinces mathematics students of the intrinsic interest of statistics and probability, and statistics students that the language of mathematics can bring real insight and clarity to their subject. He helps students build the intuition needed, in a presentation enriched with examples drawn from all manner of applications, e.g., genetics, filtering, the Black–Scholes option-pricing formula, quantum probability and computing, and classical and modern statistical models. Statistics chapters present both the Frequentist and Bayesian approaches, emphasising Confidence Intervals rather than Hypothesis Test, and include Gibbs-sampling techniques for the practical implementation of Bayesian methods. A central chapter gives the theory of Linear Regression and ANOVA, and explains how MCMC methods allow greater flexibility in modelling. C or WinBUGS code is provided for computational examples and simulations. Many exercises are included; hints or solutions are often provided. https://www.cambridge.org/core/books/weighing-the-odds/1108F5B7FBD61ECD46B13368691358C3#fndtn-information
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Item type Current library Collection Call number Status Date due Barcode Item holds
Book Book Ahmedabad General Stacks Non-fiction 519.2 W4W3 (Browse shelf(Opens below)) Available 195756
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Statistics do not lie, nor is probability paradoxical. You just have to have the right intuition. In this lively look at both subjects, David Williams convinces mathematics students of the intrinsic interest of statistics and probability, and statistics students that the language of mathematics can bring real insight and clarity to their subject. He helps students build the intuition needed, in a presentation enriched with examples drawn from all manner of applications, e.g., genetics, filtering, the Black–Scholes option-pricing formula, quantum probability and computing, and classical and modern statistical models. Statistics chapters present both the Frequentist and Bayesian approaches, emphasising Confidence Intervals rather than Hypothesis Test, and include Gibbs-sampling techniques for the practical implementation of Bayesian methods. A central chapter gives the theory of Linear Regression and ANOVA, and explains how MCMC methods allow greater flexibility in modelling. C or WinBUGS code is provided for computational examples and simulations. Many exercises are included; hints or solutions are often provided.


https://www.cambridge.org/core/books/weighing-the-odds/1108F5B7FBD61ECD46B13368691358C3#fndtn-information

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