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Discrete Choices,

Edition 1 A Comprehensive Guide to Distributions and Inference for Categorical Data

Editors: By Jiju Gillariose, Joshin Joseph and Christophe Chesneau

Publication Date: 01 Mar 2027

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Discrete Choices: A Comprehensive Guide to Distributions and Inference for Categorical Data provides a unified and modern treatment of probability, inference, and modeling for categorical and discrete data. Although categorical outcomes are ubiquitous across statistics, data science, economics, epidemiology, and the social sciences, existing resources often treat these topics in fragmented ways, separating probability theory, inference, diagnostics, and applications into distinct texts. This book addresses that gap by presenting a coherent framework that begins with probability foundations and sampling theory, develops classical and hierarchical discrete distributions, and advances to modern likelihood-based and Bayesian inference. Core topics include Bernoulli, Binomial, Multinomial, and Poisson models; compound distributions such as the Beta–Binomial and Dirichlet–Multinomial; resampling methods; model diagnostics; and principled model selection. Advanced chapters extend this framework to high-dimensional categorical data, compositional data, network and relational structures, causal inference with multi-valued treatments, and fairness and bias diagnostics in categorical prediction. Extensive case studies and interactive hands-on data labs illustrate real-world applications in health, the social sciences, marketing, and text analytics, emphasizing reproducible and applied workflows.

Balancing theoretical rigor with practical relevance, Discrete Choices: A Comprehensive Guide to Distributions and Inference for Categorical Data is suitable both as a graduate-level textbook and as a professional reference for researchers and practitioners working with discrete data.

Key Features

Introduces a clear and didactic understanding of essential concepts in statistics and data science, including categorical data and statistical inference
Includes numerous illustrations of theoretical concepts and worked examples, which provide explanations and additional context
Aligns with commonly offered upper-level courses in statistics, data science, and related topics in the field
Serves as a valuable resource for students and instructors and as solid foundational material with a unified approach for early-stage researchers
Offers ancillary support, including an Instructor’s Solutions Manual and additional R and Python programming study resources for students

About the author

By Jiju Gillariose, Assistant Professor, St. Teresa’s College, Ernakulam,, India; Joshin Joseph, Assistant Professor, School of Commerce and Professional Studies at Marian College Kuttikkanam (Autonomous), Kerala, India and Christophe Chesneau, University of Caen-Normandie, France

Part I Foundations and Core Methods
1. Foundations of Categorical Data
2. Core Distributions for Discrete Outcomes
3. Hierarchical & Conjugate Families
4. Statistical Inference
5. Model Diagnostics and Selection

Part II Advanced Topics and Applications
6. Advanced and Emerging Topics
7. Applications Across Fields
8. Categorical Treatments and Fairness in Prediction
9. Case Studies and Data Labs

Discrete Choices,

Editors: By Jiju Gillariose, Joshin Joseph and Christophe Chesneau

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