Free ebook Practical Bayesian Statistics for Real-World Decisions: From Intuition to Implementation

Course content

Bayesian Thinking as a Decision Toolkit

From Beliefs to Evidence: Priors, Likelihood, and Posteriors

Interpreting Uncertainty: Credible Intervals and Decision-Relevant Probabilities

Mini Case Study: Updating a Belief with a Simple Beta–Binomial Calculation

Building Practical Bayesian Models for Proportions and Rates

Mini Case Study: Estimating Conversion Rate with Posterior Mean and Credible Interval

Bayesian A/B Testing Beyond p-Values

Decision Metrics for Experiments: Probability of Superiority, Expected Loss, and Thresholds

Stopping Rules and Sequential Learning Without Statistical Gymnastics

Mini Case Study: Choosing a Variant Using Expected Loss and a Business Cost Model

Regression with Uncertainty: Predictive Distributions and Actionable Forecasts

Choosing Priors That Help Rather Than Harm

Weakly Informative Priors and When Priors Dominate the Data

Stress-Testing Assumptions: Prior Sensitivity Analysis for Real Decisions

Mini Case Study: How Different Priors Change a Product Decision Under Sparse Data

Hierarchical Modeling for Small Samples and Many Groups

Partial Pooling in Practice: Multi-Store Sales, Cohorts, and Class Performance

Mini Case Study: Ranking Stores with Shrinkage and Quantified Uncertainty

Model Checking and Calibration You Can Actually Use

Posterior Predictive Checks, Outliers, and Detecting Overfitting

Mini Case Study: Diagnosing a Miscalibrated Model with Posterior Predictive Simulations

Computation in Plain Language: MCMC and Variational Inference Concepts

Reading Diagnostics Without Heavy Math: Chains, Convergence, and Effective Sample Size

Practical Python Pseudocode Patterns for Bayesian Workflows

Reporting Bayesian Results for Non-Technical Stakeholders

Decision Memo Templates: What to Say, What to Show, and Common Pitfalls

Ethical Communication of Uncertainty and Avoiding Manipulative Framing

Capstone Project: Designing a Bayesian Experiment Plan and Writing a One-Page Decision Memo

Course Description

Practical Bayesian Statistics for Real-World Decisions: From Intuition to Implementation is a free ebook course in Basic studies and Statistics that helps you turn uncertainty into clear, defensible choices. Instead of treating data analysis as a ritual of pass or fail outcomes, you will learn Bayesian thinking as a decision toolkit that connects what you believe today with what the evidence says tomorrow.

You will build intuition for priors, likelihood, and posteriors and learn how belief updating works in practice, including a simple beta binomial calculation that makes uncertainty tangible. From there, the course moves into decision-relevant probabilities and credible intervals so you can answer questions like what is the chance the conversion rate is above a target, or how confident you should be before changing a product or policy.

The ebook emphasizes practical Bayesian models for proportions and rates, Bayesian A B testing beyond p values, and experiment decision metrics such as probability of superiority, expected loss, and business thresholds. You will see how sequential learning and sensible stopping rules can be handled without statistical gymnastics, and how to choose a variant using expected loss tied to a cost model that mirrors real organizational tradeoffs.

To support real workflows, you will learn regression with uncertainty through predictive distributions and actionable forecasts, along with guidance on choosing priors that help rather than harm. Weakly informative priors, prior dominance under sparse data, and prior sensitivity analysis are explained with mini case studies so you can stress test assumptions before they stress your decisions. When data is limited across many groups, you will use hierarchical modeling and partial pooling to compare stores, cohorts, or class performance with shrinkage and quantified uncertainty.

You will also learn model checking and calibration techniques you can actually use, including posterior predictive checks, outlier handling, and ways to detect overfitting. Computation is introduced in plain language with MCMC and variational inference concepts, plus a practical way to read diagnostics like chains, convergence, and effective sample size. The course includes Python pseudocode patterns for Bayesian workflows and finishes with reporting skills for non technical stakeholders, decision memo templates, and ethical communication of uncertainty. Start the course now and learn to make better decisions with Bayesian statistics, from intuition to implementation.