This post is by Eric.

We have a number of Stan related events in the pipeline. On 22 Nov, Ben Goodrich and I will be holding a free webinar called Introduction to Bayesian Computation Using the rstanarm R Package.

Here is the abstract:

The goal of the rstanarm package is to make it easier to use Bayesian estimation for most common regression models via Stan while preserving the traditional syntax that is used for specifying models in R and R packages like lme4. In this webinar, Ben Goodrich, one of the developers of rstanarm, will introduce the most salient features of the package.

To demonstrate these features, we will fit a model to loan repayments data from Lending Club and show why, in order to make rational decisions for loan approval or interest rate determination, we need a full posterior distribution as opposed to point predictions available in non-Bayesian statistical software.

As part of the upcoming StanCon 2017, we will be teaching a number of classes on Bayesian inference and statistical modeling. Here is the lineup:

- Introduction to Bayesian Inference with Stan (2 days): 19 – 20 Jan 2017
- Stan for Finance and Econometrics (1 day): 20 Jan 2017
- Stan for Pharmacometrics (1 day): 20 Jan 2017
- Advanced Stan: Programming, Debugging, Optimizing (1 day): 20 Jan 2017

For Stan users and readers of this blog, please use the code “stanusers” to get a 10% discount.

We hope to see many of you online and in person.

For any that have registered for StanCon, you’ll receive a 15% discount code for the training courses.

Is the Stan team aware of this incredibly cool new black-box variational algorithm, Stein Variational Gradient Descent, and if so, will it become available in Stan?