### 08-13, 12:00–12:40 (Asia/Yerevan), 213W PAB

The parameters in a statistical model are not always identified by the data. In Bayesian analysis, this

problem remains unnoticed because of prior assumptions. It is crucial to find out whether the data

determine the marginal posterior parameters. As the famous mathematician George Box stated,

“Since all models are wrong the scientist must be alert to what is importantly wrong.”

The R package ed4bhm, which is available on GitHub allows to examine the empirical determinacy of

posterior parameters for the models fitted with well-known Bayesian techniques.

- The
**topic**(the WHAT) and WHY it is**interesting**:

The parameters in a statistical model are not always identified by the data. In Bayesian analysis, this

problem remains unnoticed because of prior assumptions. It is crucial to find out whether the data

determine the posterior parameters. In particular, it is important to learn to what extent the spread

and the location of marginal posterior distribution of the parameters are determined by the data.

The R package ed4bhm allows to investigate the empirical determinacy of marginal posterior

parameters, their spread and location. During this talk, I will showcase the functionality of the

package ed4bhm with an application of Bayesian logistic regression to Bacterial resistance data.

Public links to notes and helpful material will be provided on GitHub.

- The
**audience**to WHOM the talk is addressed

This talk is interesting for everyone who is working or is interested in Bayesian analysis.

- The
**TAKEAWAY**, a.k.a. what will I learn

The participants will learn about

1. The typical problems in a Bayesian Hierarchical Model (BHM)

2. The theory behind the empirical determinacy of posterior parameters in BHMs

3. How to fit basic BHM in R

4. How to apply the R package ed4bhm and how to interpret the results.

- Any
**background**knowledge needed

Some basic knowledge in statistics and Bayesian statistics would be helpful.

**Prior Knowledge Expected**–

No previous knowledge expected

Sona Hunanyan is a scientist-statistician at Philip Morris Armenia. Apart from that, she is working on research projects in statistical analysis of machine learning algorithms within the framework of the FAST Advance STEM program.

Sona completed a Master's Degree in Applied Mathematics at EPFL Lausanne. Beside her studies, she gained practical experience while working as a quantitative analyst at Nuclear Power Plant Goesgen, Switzerland. Sona holds a PhD in Biostatistics from the University of Zurich. The main topic of her PhD thesis was Bayesian hierarchical modeling and software development in R.