#### Event Title

Bayes Theory, Priors, Posteriors, and Bayesian Data Analyses Applied to Probability Predictions

#### Location

McCartney Hall 104

#### Start Date

2-8-2019 11:00 AM

#### End Date

2-8-2019 12:00 PM

#### Description

What is the probability that there is a certain "state of the world" given a particular indicator observation? How can prior knowledge of the world be applied to present-moment probabilities? Bayes Theorem has been applied to various types of small data sets for years, and its application applies to educational data as well. This presentation introduces what Bayes Theorem says, and then demonstrates how it can be applied through the RapidMiner Studio to understand probabilities and likelihoods. This will also address what the “naïve” means in a Naïve Bayes Theory application…and what non-naïve Bayes computations may look like.

Bayes Theory, Priors, Posteriors, and Bayesian Data Analyses Applied to Probability Predictions

McCartney Hall 104

What is the probability that there is a certain "state of the world" given a particular indicator observation? How can prior knowledge of the world be applied to present-moment probabilities? Bayes Theorem has been applied to various types of small data sets for years, and its application applies to educational data as well. This presentation introduces what Bayes Theorem says, and then demonstrates how it can be applied through the RapidMiner Studio to understand probabilities and likelihoods. This will also address what the “naïve” means in a Naïve Bayes Theory application…and what non-naïve Bayes computations may look like.