Probability and Statistical Inference: From Basic Principles to Advanced Models

Probability and Statistical Inference: From Basic Principles to Advanced Models is a comprehensive textbook that covers probability, distribution theory, and inference, providing a solid foundation for understanding data analysis and statistical modelling. It introduces techniques relevant to modern practitioners while respecting the history of statistical inference and placing abstract concepts in a practical context. The book is suitable for students with a first course in probability and statistics and can also serve as a foundation for advanced undergraduate or postgraduate courses. It is also useful for data scientists, researchers, and applied practitioners who want to understand the theory behind statistical methods.

Format: Paperback / softback
Length: 424 pages
Publication date: 26 September 2022
Publisher: Taylor & Francis Ltd

Probability and Statistical Inference: From Basic Principles to Advanced Models delves into the core aspects of probability, distribution theory, and inference, essential for a comprehensive understanding of data analysis and statistical modeling. It presents these topics in a user-friendly manner, without compromising mathematical rigor, bridging the gap between introductory books and advanced graduate-level texts. The book introduces and explores techniques that are relevant to modern practitioners, while honoring the historical roots of statistical inference. It aims to provide a thorough grounding in both the theory and application of statistics, with even the abstract concepts placed in a practical context.

Key Features:

Comprehensive Introduction to Mathematical Probability, Random Variables, and Distribution Theory: The book offers a thorough introduction to mathematical probability, random variables, and distribution theory, providing a solid foundation for further exploration.

Concise but Broad Account of Statistical Modeling: It covers a wide range of statistical modeling topics, including generalised linear models, survival analysis, time series, and random processes.

Extensive Discussion of Classical Statistics and Likelihood-Based Inference: The book delves into the key concepts of classical statistics, such as point estimation, interval estimation, and hypothesis testing, along with the main techniques in likelihood-based inference.

Detailed Introduction to Bayesian Statistics and Associated Topics: It provides a comprehensive introduction to Bayesian statistics, covering topics such as prior distributions, posterior distributions, and Bayesian inference.

Practical Illustration of Computational Methods in Modern Statistical Inference: The book includes practical illustrations of some of the main computational methods used in modern statistical inference, such as simulation, bootstrap, and Markov chain Monte Carlo (MCMC).

This book is designed for students who have already completed a first course in probability and statistics and seek to deepen and broaden their understanding of the subject. It can serve as a foundation for advanced undergraduate or postgraduate courses. Our goal is to challenge and excite the more mathematically able students, while providing explanations of statistical concepts that are more detailed and nuanced.

Weight: 668g
Dimension: 156 x 235 x 29 (mm)
ISBN-13: 9780367749125