This course will introduce you to the basic ideas of Bayesian Statistics. In Bayesian statistics, population parameters are considered random variables having probability distributions. These probabilities measure “degree of belief”. The rules of probability (Bayes’ theorem) are used to revise our belief, given the observed data. Bayesian methods will be contrasted with the comparable frequentist methods, demonstrating the advantages this approach offers. Bayes’ theorem gives the general way to find the predictive distribution of future observations. There is no such general method in frequentist statistics, only a collection of methods that sometimes work.
Dr. William M. Bolstad
Dr. William M. Bolstad is a Professor at the University of Waikato, New Zealand, Dept. of Statistics, and has 30 years of teaching experience. Dr. Bolstad is the author of Introduction to Bayesian Statistics, 2nd Edition (the course text), and has pioneered the use of Bayesian methods in teaching the first year statistics course.