These lecture notes are a work in progress, and do not contain everything we cover in the course. There are many things that are important and examinable, and will be only discussed in …

Over sixty author videos provide definitions, tips, and examples surrounding the key topics of each chapter. Test yourself! Answers to the in-text problem sets will help you check your work …

In writing this, we hope that it may be used on its own as an open-access introduction to Bayesian inference using R for anyone interested in learning about Bayesian statistics. Materials and …

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A decision maker, for example, somewhat knowledgable about statistics and probability, may want to know what are the odds that a parameter lies in one region versus another.

The notes are available on Blackboard. The slides will be posted after each topic is finished, along with any other course material – such as model solutions to assignment questions, …

Find an unbiased estimator of θ. Find the maximum likelihood estimate (MLE) of θ by looking at the likelihood of the data. If you cannot remember the definition of an unbiased estimator or …

Consider an experiment to estimate the difference in height between men and women in the Australian population. We take a random sample of 10 men and 10 women and measure their …

Statistical approaches to parameter estimation and hypothesis testing which use prior distributions over parameters are known as Bayesian methods. The following notes brie y summarize some …

4.4 Bayesian (smoothed) Naive Bayes We can also apply smoothing to our Naive Bayes model.