Date & Time: 2023-12-06T17:30:00Z
Host: Dr. Thomas Wiecki
Register for Zoom link:
Bayesian hierarchical modeling has been a staple in the world of discrete choice models for decades. During this time, the scientific community has generated countless research papers and developed numerous tools and libraries (with around 99% of them written in R) to tackle common market research questions by constructing classical models using HBayes and MCMC.
However, in today’s fast-paced commercial landscape dominated by cloud-based solutions, startups, and SaaS, many of these traditional solutions and libraries are falling short. Users often encounter challenges related to technical integration, licensing restrictions, and more. In this webinar, we’ll take you through our journey in building a Bayesian SaaS product. Shedding light on the pain points we’ve experienced: data collection and encoding, model result interpretation, and performance issues We will try to explain why, in this evolving landscape, PyMC emerges as the singular, user-friendly solution that bridges the gap between complex Bayesian modeling and practical usability.
Outline of Talk / Agenda:
- 5 min: Intro to PyMC Labs and speakers
- 45 min: Presentation, panel discussion
- 10 min: Q&A
Matthew Johnston (Founder and CEO at the EPIC Conjoint)
Matt Johnston is a seasoned commercial marketing professional with over 20 years of pricing, product and segmentation experience. Matt has an extensive background in telecommunications as former Head of Pricing at Telefónica Ireland and Ooredoo Group.
Pavel Knorr (CTO at the EPIC Conjoint)
Pavel is a software developer and architect with more than ten years of professional experience and a strong foundation in machine learning and math. Currently, he holds the position of Chief Technical Officer at EPICConjoint, a startup specializing in market research and conjoint analysis. He is obtaining his Ph.D. in applied math.
Tomas Capretto (Principal Data Scientist at PyMC Labs)
Tomi is a Principal Data Scientist at PyMC Labs, part-time PhD student, and statistics instructor at Universidad Nacional de Rosario. With a dedicated commitment to open-source development, Tomi’s focus lies in the Bambi project.
Connect with Tomi:
GitHub: tomicapretto (Tomás Capretto) · GitHub
Dr. Thomas Wiecki (PyMC Labs)
Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs – the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.
Code of Conduct:
Please note that participants are expected to abide by PyMC’s Code of Conduct.