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  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Bayesian workflow
  name:
    name part: Gelman, Andrew
    role:
      role term: creator
    role:
      role term: author.
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      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Vehtari, Aki
    role:
      role term: author.
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: McElreath, Richard
    name part: 1973-
    role:
      role term: author.
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Simpson, Daniel
    name part: (Statistician)
    role:
      role term: author.
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Margossian, Charles C.
    role:
      role term: author.
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Yao, Yuling
    role:
      role term: author.
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Kennedy, Lauren
    name part: Dr
    role:
      role term: author.
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Gabry, Jonah
    role:
      role term: author.
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Bürkner, Paul-Christian
    role:
      role term: author.
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Modrák, Martin
    role:
      role term: author.
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Leos Barajas, Vianey
    role:
      role term: author.
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: flu
    date issued: 2026
    copyright date: 2026
    edition: First edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : illustrations
  abstract: Bayesian statistics and statistical practice have evolved over the years, driven by advancements in theory, methods, and computational tools. Bayesian Workflow explores the intricate workflows of applied Bayesian statistics, aiming to uncover the tacit knowledge often overlooked in published papers and textbooks. By systematizing the process of Bayesian model development, the book seeks to improve applied analyses and inspire future innovations in theory, methods, and software. It emphasizes the importance of iterative model building, model checking, computational troubleshooting, and simulated-data experimentation, offering a comprehensive perspective on statistical analysis.Through detailed examples and practical guidance, the book bridges the gap between theory and application, empowering practitioners and researchers to navigate the complexities of Bayesian inference. It is not a checklist or cookbook but a flexible framework for understanding and resolving challenges in statistical modeling and decision-making under uncertainty.Features Covers all aspects of Bayesian statistical workflow, including model building, inference, validation, troubleshooting, and understanding Demonstrates iterative model development and computational problem-solving through real-world case studies Explores computational challenges, calibration checking, and connections between modeling and computation Highlights the importance of checking models under diverse conditions to understand their limitations and improve their robustness Discusses how Bayesian principles apply to non-Bayesian methods in statistics and machine learning Includes code snippets, exercises, and links to full datasets and code in R and Stan, with applicability to other programming environments like Python and Julia This book is designed for practitioners of applied Bayesian statistics, particularly users of probabilistic programming languages such as Stan, as well as developers of methods and software tailored to…
  table of contents: Part 1: From Bayesian inference to Bayesian workflow. 1 Bayesian theory and Bayesian practice. 2 Statistical modeling and workflow. 3 Computational tools. 4 Introduction to workflow: Modeling performance on a multiple choice exam. Part 2: Statistical workflow. 5 Building statistical models. 6 Using simulations to capture uncertainty. 7 Prediction, generalization, and causal inference/ 8 Visualizing and checking fitted models. 9 Comparing and improving models. 10 Statistical inference and scientific inference. Part 3: Computational workflow. 11 Fitting statistical models. 12 Diagnosing and fixing problems with fitting. 13 Approximate algorithms and approximate models. 14 Simulation-based calibration checking. 15 Statistical modeling as software development. Part 4: Case studies. 16 Coding a series of models: Simulated data of movie ratings. 17 Prior specification for regression models: Reanalysis of a sleep study.18 Predictive model checking and comparison: Clinical trial. 19 Building up to a hierarchical model: Coronavirus testing. 20 Using a fitted model for decision analysis: Mixture model for time series competition. 21 Posterior predictive checking: Stochastic learning in dogs. 22 Incremental development and testing: Black cat adoptions. 23 Debugging a model: World Cup football. 24 Leave-one-out cross validation model checking and comparison: Roaches. 25 Model building and expansion: Golf putting. 26 Model building with latent variables: Markov models for animal movement. 27 Model building: Time-series decomposition for birthdays. 28 Models for regression coefficients and variable selection: Student grades. 29 Funnel problem with latent variables: No vehicles in the park. 30 Computational challenge of multimodality: Differential equation for planetary motion. 31 Simulation-based calibration checking in model development workflow.
  target audience: Academic, Postgraduate, and Professional Practice & Development
  target audience: adult
  note: Andrew Gelman, Aki Vehtari, and Richard McElreath with Daniel Simpson, Charles C. Margossian, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner, Martin Modrák, and Vianey Leos Barajas.
  note: "CRC Press".
  note: Includes bibliographical references and indexes.
  subject:
    topic: Bayesian statistical decision theory
  subject:
    topic: Decision making
    topic: Statistical methods
  subject:
    topic: Bayesian statistical decision theory
  subject:
    topic: Decision making
    topic: Statistical methods
  classification: QA279.5 .G4523 2026
  classification: 519.5
  classification: QA 279.5 G319 2026eb
  identifier: 9781003044024
  identifier: 9780367490140
  identifier: 9780367490188
  identifier: 1003044026
  identifier: 9781040729779 Taylor & Francis
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: TYFRS
    record creation date: 260527
    record change date: 20260702150641.9
    record identifier: in00005786707
    language of cataloging:
      language term: eng