Hello. I'm Herbie Lee, Professor of Statistics at the University of California Santa Cruz. Welcome to this course on Bayesian Statistics. There are two main philosophies of probability and statistics, Bayesian and Frequentist. Most existing classes focus on a Frequentist approach, here we will learn the Bayesian approach and some of its benefits. In particular, the Bayesian approach is better for dealing with uncertainty. Both in terms in being able to quantify uncertainty, and also for being able to combine uncertainties in a coherent manner. The interpretations of intervals make much more sense in the Bayesian paragon. This course is meant to be a largely self contained, thus it starts with a brief review of some few concepts, probability, based term, and distributions. We will then have a quick review of frequent distant inference and then start on Bayesian inference with comparisons with connections between the two. In the third module we will learn more about priors and posteriors for discreet distributions. The Bernoulli, Binomial and Poisson distributions. Finally in the fourth module we will discuss continuous distributions, especially the normal or Gaussian distribution and apply it to linear regression. In this course you will learn the difference between Bayesian and Frequentist inference. Key concepts of Bayesian inference. And how to perform Bayesian inference in simple cases. Some calculations can be done by hand others will be done on the computer. You will have the choice using R or Excel. You are expected to choose one of R or Excel, but you can do both if that interests you. Otherwise you can pick from the available computer demonstrations which are listed as lectures. You don't need to watch both the R and Excel lectures. Just the ones for the software you have chosen. The format of this class is a little different from a conventional class. We'll have less in the video, and more in the exercises. Because learning by doing is more effective than just watching a video of mathematical concepts. These are what would be homework assignments in an in person course. But there isn't a label in Coursera for homework so they are labeled as quizzes, but do think of them as homework assignments. We've tried to include a lot of additional explanation in the feedback, both for correct and incorrect responses. So you may need to try these homework assignments several times to get a passing grade. In the feedback that's provided to assist you in learning the material. Items labeled background material should be read before watching the video lecture. Items labeled supplementary material follow the lecture and are more optional. One note on video resolution, we recommend that you watch at the highest resolution. You can see this from the settings the gear icon on the lower right, under video quality, choose high. You may need to watch a video more than once. If you're confused after the first watching, check the background and supplemental materials again and then try watching the video again. You may even attempt some of the exercises and then go back and watch the video. Learning is an iterative process, so trying to work the exercises may provide context that helps make the video more understandable. In terms of prerequisites and expectations, I expect that you've taken an introductory statistics course and a calculus course. In both cases that you've seen the concepts before, but don't necessarily remember everything. I'll get refreshers for the statistical concepts, such as probability, central limit theorem, conference intervals and regression. But I won't be providing a complete introduction. Similarly I expect that you understand concept with an interval and a derivative and I will be using them as calculations, but none of the mandatory exercises will require you to integrate or differentiate. Although some may require finding a simple area under curve, like finding the area of rectangle. A few of the honors exercises will require calculus. For those people who do remember how to integrate and differentiate. So my basic expectation is that you've taken calculus, an introductory statistics course at some point in your life. But it may be a long time ago and you've forgotten all the details. That's fine. I just need you to have seen it before so that when I use some of these it will trigger some memories about the concepts and the bigger picture, so that the exact calculations aren't totally foreign for you.