Title
Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica Support,Used
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Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain MonteCarlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a selfcontained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.
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We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
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To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What is the main focus of 'Bayesian Logical Data Analysis for the Physical Sciences'? A: The book focuses on Bayesian inference as a unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses based on current knowledge.
- Q: What kind of examples are included in this book? A: It includes many worked examples and problem sets to illustrate the underlying concepts of Bayesian analysis.
- Q: Does the book cover practical implementation of Bayesian methods? A: Yes, it discusses implementation techniques, including Markov chain Monte-Carlo integration and both linear and nonlinear model fitting.
- Q: Are there any specific topics that are heavily covered? A: The book provides extensive coverage of spectral analysis, including a self-contained introduction to Fourier and discrete Fourier methods.
- Q: Is there content related to frequentist methods? A: Yes, there are three chapters dedicated to frequentist methods to help bridge the gap between frequentist and Bayesian approaches.
- Q: What is the condition of the book? A: The book is classified as a used book in good condition.
- Q: How many pages does the book have? A: The book contains 488 pages.
- Q: What type of binding does this book have? A: The book is available in hardcover binding.
- Q: Who is the author of this book? A: The book is authored by Phil Gregory.
- Q: Where can I find additional resources related to this book? A: Supporting Mathematica notebooks and additional resources can be found at www.cambridge.org/9780521150125.