Bayesian analysis with Excel and R / Conrad Carlberg.
Material type:
TextPublisher: Hoboken : Pearson Education, Inc, 2022Edition: 1Description: xvi, 169 pages : illustrations ; 24 cmContent type: - text
- unmediated
- volume
- 9780137580989
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|---|
| Books | Main Library Reserve | Reference | 519.5 C278 2023 (Browse shelf(Opens below)) | Available | 3BPSU00017950X |
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| 511.3/13 T882 2024 Fuzzy logic : applications in artificial intelligence, big data, and machine learning / | 515 B167 2024 Calculus : a comprehensive course / | 515.35 L987 2024 Introduction to differential equations / | 519.5 C278 2023 Bayesian analysis with Excel and R / | 519.50285/5133 G363 2023 R data analysis without programming : explanation and interpretation / | 519.50285/5133 St859 2018 Statistics with R : a beginner's guide / | 570.15195 N138 2022 Introduction to biostatistics with R / |
Includes index.
"This book explains the main differences between the basis for traditional, "frequentist" statistical methods and the basis for Bayesian approaches. The frequentist derives inferences from imagined populations and samples. In contrast, the Bayesian derives inferences from populations that are actually generated and then used as a source of samples. Three methods of generating Bayesian models are discussed: grid approximation, quadratic approximation and Markov Chain Monte Carlo (MCMC). The book walks the reader through R code that exemplifies each method, and shows how VBA and Excel can together perform grid approximation. The book recommends that the reader adopt Bayesian methods as an accompaniment to frequentist techniques"-- Provided by publisher.
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