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Bayesian Statistics and Probabilistic Programming in R
Bayesian statistics is an alternative way of working with probability and statistics with a strong mathematical foundation. This course will teach you to implement Bayesian statistical models in R.
What you'll learn
Bayesian statistics provide an alternative way of interpreting probability and an alternative way of dealing with statistics, compared to the more common frequentist interpretation. In this course, Bayesian Statistics and Probabilistic Programming in R, you’ll gain the ability to implement and interpret Bayesian models in R using the brms package. First, you’ll explore the Bayesian theorem and its mathematical underpinnings. Next, you’ll discover how to select prior probabilities and update them with experimental data to produce a model which explains the experimental data and can be used for further inference. Finally, you’ll learn how to implement those models in R, using the brms package. When you’re finished with this course, you’ll have the skills and knowledge of Bayesian statistics needed to perform statistical analysis on a wide variety of real-world data sets and problems.
Table of contents
About the author
Gail Shaw is a Data Platform MVP and holds the MCM certification for SQL Server. Her specialties are in performance tuning and database recovery for SQL Server. She is a frequent poster on the SQL Server Central forums, writes articles for both SQLServerCentral.com and Simple-Talk.com, and often speaks at SQLBits and the PASS Community Summit.
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