Hammers and Nails: An Introduction to Bayesian Statistics Through Applications – Hardcover

Hammers and Nails is an example-driven guide to Bayesian statistics that uses small, memorable datasets to show how the well-known flexibility of Bayesian modelling can be used to answer the questions researchers and policy makers may actually ask. The book moves from fundamentals to numerical methods and advanced applications. The latter part covers, among other topics, survival analysis, zero-inflated and other mixture models, and the treatment of missing data. While the basics of regression modelling are inevitably covered as well, examples show how the Bayesian regression framework can be used to answer practical questions beyond the interpretation of model coefficients.

Throughout the book, the research question informs every aspect of the modelling process. The importance of assumptions and diagnostics is made explicit through numerous worked examples, with R code provided. Compact datasets keep ideas visible and place the entire modelling process within a relatable practical context, drawing on the author’s many years of experience as a statistical consultant.

Key Features:

• Contains concrete, real-world case studies spanning safe speed limits, identifying daily temperature extremes, traffic flow, gym attendance, engineering properties of eucalyptus trees, seed germination, the performance of 3D printers, and the treatment of missing data.
• Introduces a comprehensive computational toolkit covering sampling methods, Monte Carlo integration, MCMC methods, particle filters, and approximate Bayesian computation.
• Provides algorithms and R code for Bayesian models applied to a wide range of worked examples.
• Includes end-of-chapter exercises, a consolidated list of algorithms, and appendices on probability theory, probability functions, and key distributions, bridging theory, derivations, and computation.

This book is intended for applied researchers, statisticians, data scientists, and postgraduate students in quantitative fields who are interested in practical, research-question-driven modelling. Readers with a grounding in probability, calculus, and programming will find the material suitable for graduate study, self-study, and professional practice.

Additional materials, including datasets used in the examples, solutions to exercises, and errata, are available at https://github.com/ANOVA-droid/bayesian-hammersand-nails .