[Online] Software Engineering for Probabilistic Programming

## Link to join the webinar here:

https://www.bigmarker.com/neo4j/Data-Umbrella-Webinar

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Video Recording
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This event will be recorded and placed on our YouTube. We usually have it up within 24 hours of the event. Subscribe to our YouTube and set your notifications:
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Time
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9am PT / 12pm ET / 7pm EAT / 9:30 PM IST

## Speaker

Mitzi Morris

## Talk Level

Intermediate

## Pre-reqs

None required.
You can look at:

1. Intro to statistical modeling: https://github.com/rmcelreath/stat_rethinking_2022
2. Intro Stan case study: https://mc-stan.org/users/documentation/case-studies/pool-binary-trials.html

## Prep Work

Experience with datasets as data frames in Python or R and using regression models or ML techniques for either analysis or prediction.

## Resources

Stan User's Guide: https://mc-stan.org/docs/stan-users-guide/index.html,
CmdStanPy docs: https://mc-stan.org/cmdstanpy/,

## Event

Our goal is to provide a wholistic introduction to Bayesian modeling for data scientists as well as to demonstrate best practices for Bayesian data analysis and prediction. We use the Stan probabilistic programming language and inference engines to do inference, and the plotnine package for visualization. Applied statistical modeling starts with the question we are trying to answer and the available data. We must understand both the data and the analysis goals before proceeding to model building. We use the Stan probabilistic programming language and inference engine to build and fit our models, via modern Python interface CmdStanPy, and we use the Python package plotnine for visualization.

## Speaker

Mitzi Morris is a member of the Stan development team. As a software engineer, she has contributed to the core Stan C++ infrastructure and authored CmdStanPy . As a Bayesian data analyst, she has helped epidemiologists use Bayesian method for disease mapping. Her background is software engineering and natural language processing, with a detour through genomics and bioinformatics.

GitHub: https://github.com/mitzimorris

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