My name is Angie Moon, and I was a StanCon Helsinki attendee. After the conference, motivated by Stan’s ideal and energy, I decided to build a Stan user group in my country (South Korea). During the process, I needed advice on mainly two points: reference material recommendation & feedback on Stan Korea’s future plans. Our activities so far can be divided into two parts, as explained below:
1. Stan user groups in Korea: Foundation & Events and Talks to attract users
Founded Stan Korea: Created Stan Korea (facebook group) with two partners. I am in charge, Kyungmin Kwon will make contents, and Byungkwon Lee will help manage the group.
STEM Tech Square: STEM is an official Honor Society at the Seoul National University (SNU) Engineering School, and Tech Square is the annual event for STEM members to examine and discuss a specific topic; this year upon my suggestion we have chosen 'Bayesian Inference using Stan’ as our topic, and around 20 people have shown interest so far. I would like to attract STEM members to be the initial members of Stan Korea especially since they have great diversity in their majors and are highly competent students (additional information on STEM is listed at the end).
GLEAP X STEM Conference: GLEAP members are also great candidates for initial Stan Korea members. GLEAP is SNU Natural Science Honor Society. Each year, GLEAP and STEM jointly hold a conference for the purposes of academic exchange. This year the conference takes place on November 24th and I am planning to give two talks. My first talk is titled “Generative Model for Inverse Molecular Design” and its main reference is the article “Inverse molecular design using machine learning: Generative models for matter engineering” which was published on 27 July 2018 in Nature. The main focus of the article is deep generative models, but I am planning to put more weight on Bayesian sampling and its application as an example of generative model. My second talk is titled “Demand Time Series Forecasting with Bayesian Inference and Stan”, which is based on my experience for the last two years in demand forecasting.
Reference materials for Tech Square and conference talks: slides, books, video lectures, and articles.
I read through Jonah’s reading list*, Stan manual, and previous Stan slides made by core developers, but I need prioritization (*https://github.com/jgabry/stancon2018helsinki_intro/blob/master/reading-list.md).
2. Creating Bayesian Educational Contents in Korean: Translation & Video lectures
Stan material translation: So far we have translated “A probabilistic programming language” and “Stan: a probabilistic programming language for Bayesian Inference and Optimization”. We would like to introduce hierarchical model, and are planning to translate “Multilevel (Hierarchical) Modeling: What It Can and Cannot Do”, as well.
Video lectures on Bayesian Inference [Theory] and [Application] (planning): We will use Youtube platform, and one lecture would be about 20-minutes. Followings are our reference materials.
Theory: Basics of Bayesian inference and Stan, Hierarchical models, Model Assessment and Selection (StanCon, Helsinki Tutorial), BDA textbook
Application: Basics of Bayesian inference and Stan, Productization of Stan (StanCon, Helsinki Tutorial) + Example Models from Stan reference manual 2.17.0
1. For video lectures, how could the [Theory] and [Application] blended well in one play list? Would it be better to have two separate play lists?
I think statisticians would be more interested in [Theory] and users from other fields would be more interested in [Application], where they learn how to apply Bayesian Inference to their fields. Mathematical explanations would be more emphasized for [Theory] lectures whereas coding and case studies would be the main part for [Application].
2. Slides, books, lectures, and articles that we could base our video lectures on.
I have watched Ben Goodrich’s lecture on ‘Bayesian Statistics for the Social Sciences’, McElreath’s lecture on ‘Statistical Rethinking’, and Ben Lambert’s lecture on ‘A Student’s Guide to Bayesian Statistics’. Are there any further materials you would like to recommend?
and special thanks to Daniel Lee, who gave me courage to start posting on Stan forum.
STEM (additional information)