Bayesian Inference with Normal Distributions
Master Probabilistic Reasoning and Statistical Decision-Making
This book guides you through the essential principles and practical applications of Bayesian inference methods centered on normal distributions. You'll begin by understanding how Bayesian thinking differs fundamentally from classical statistics, then progressively build your skills from foundational concepts through advanced techniques. The journey takes you from basic probability theory and Bayes' theorem through conjugate priors, posterior distributions, and credible intervals. You'll discover how to construct appropriate prior distributions, interpret posterior results, and make data-driven decisions with quantified uncertainty. Real-world examples demonstrate Bayesian inference in action across quality control, medical testing, and forecasting scenarios. By the end, you'll master computational approaches like MCMC for complex problems and understand how to validate your inferences through sensitivity analysis. Whether you're analyzing experimental data, building predictive models, or making business decisions, this book provides the conceptual clarity and practical tools to apply Bayesian methods confidently and effectively.
Est. Length
207 pages (9 chapters)
Language
English
Publication date
Nov 16, 2025
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