Bayesian Statistical Concepts and Methods

Overview

Welcome to Bayesian Statistical Concepts and Methods. In this course, you will use Bayesian methods in data analysis and modeling; work with posterior distributions, distributions without closed form, directed acyclic graphs, Markov Chain Monte Carlo algorithms; and employ R and the Stan platform for statistical modeling. You will also be introduced to Bayesian hierarchical models, which are useful for the interpretation of multi-level data (sub-group versus group).

Syllabus

Course Introduction

This Specialization covers the use of statistical methods in today's business, industrial, and social environments, including several new methods and applications. H.G. Wells foresaw an era when the understanding of basic statistics would be as important for citizenship as the ability to read and write. Modern Statistics for Data-Driven Decision-Making teaches the basics of working with and interpreting data, skills necessary to succeed in Wells’s “new great complex world” that we now inhabit. In this course, learners will be able to use Bayesian methods in data analysis and modeling, to work with posterior distributions, distributions without closed form, directed acyclic graphs, and Markov chain Monte Carlo algorithms, and to use R and the Stan platform for statistical modeling. Learn more about the instructors who developed this course. Read the instructor bios and review the learning outcomes for the course.

Methods for Bayesian Simulation and Estimation

In Module 2, we will draw a Bayesian model as a graph and distinguish posterior distribution, posterior predictive distribution, and expected loss or cost. We will also calculate distributions without closed form, recognizing that we can use computational methods to draw from the distribution even when there's no straight-forward equation to define them. Be sure to review the learning objectives before beginning work in this module.

Applied Bayesian Modeling with Stan

In Module 3, we will employ R and the Stan platform for statistical modeling. You will explore Bayesian methods in data analysis and modeling; work with posterior distributions, distributions without closed form, directed acyclic graphs, and Markov Chain Monte Carlo algorithms. You will also be introduced to Bayesian hierarchical models, which estimate subgroup parameters relative to the parameters of a larger parent group. Be sure to view the course introduction video and review the learning objectives before beginning work in this module.