Understanding big social science data with Bayesian process models

Penn State IT

How can Bayesian methods provide new insights into social science data? Join Zita Oravecz for a discussion of how Bayesian statistics can be used to develop process models that describe the underlying pathways of human functioning.
Seating is limited. Please RSVP by April 2. We hope you’ll share this event with other faculty who are interested in high-performance computing and big data research at Penn State. Refreshments will be served!
This meeting will be live-streamed on Zoom at: <A HREF="https://psu.zoom.us/j/318479868" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">https://psu.zoom.us/j/318479868</A>
This event is part of the ICS CyberScience Seminars, a monthly lecture series exploring cutting-edge topics related to computational and big data research.<A HREF="https://ics.psu.edu/cyberscience-seminars" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer"> </A>
<H6 CLASS="MsoNormal">About the Speaker</H6>
<IMG ALT="Zita Oravecz" SRC="https://cdn.evbuc.com/eventlogos/197797873/zitaoravecz.jpg">
Zita Oravecz is an assistant professor of human development and family studies at Penn State and a co-hire of the Institute for CyberScience. She is also part of the Social Science Research Institute's Quantitative Developmental Systems Methodology Core. Her research focuses on creating models that capture individual differences in terms of person-specific process model parameters, and applying these models to large-scale (e.g., intensive longitudinal) data. These process models offer a framework for testing substantive theories by proposing concrete mechanisms that underlie observed data.

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Understanding big social science data with Bayesian process models

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