Sylvia Frühwirth-Schnatter

Sylvia Frühwirth-Schnatter (born May 21, 1959) is an Austrian statistician and professor of applied statistics and econometrics at the Vienna University of Economics and Business. She is known for her research in Bayesian analysis and is currently (2020) the President of the International Society for Bayesian Analysis.[1]

Sylvia Frühwirth-Schnatter
Born (1959-05-21) May 21, 1959
Vienna, Austria
NationalityAustrian
Alma materTU Wien
Known forBayesian inference, Mixture distribution, Markov chain Monte Carlo
Scientific career
FieldsStatistics
InstitutionsVienna University of Economics and Business
Websitestatmath.wu.ac.at/~fruehwirth/

Biography

Sylvia Frühwirth-Schnatter was born in 1959 in the Brigittenau district of Vienna. After attaining her doctorate in engineering mathematics from the TU Wien she held numerous academic positions, including professor of statistics at the Johannes Kepler University Linz. Since 2011, she is full professor of statistics at the Vienna University of Economics and Business.[2] Sylvia Frühwirth-Schnatter is married and mother of three sons.

Research

In her research, Sylvia Frühwirth-Schnatter inter alia explores ideas relating to Bayesian econometrics, such as efficient Markov chain Monte Carlo methods and Bayesian analysis of finite Mixture models. In 2014, she co-developed a Bayesian approach to Exploratory Factor Analysis with Nobel Prize winning economist James Heckman.[3] She is a quadruple winner of the WU Best Paper Award[4] and recipient of the DeGroot Prize[5] bestowed by the International Society for Bayesian Analysis for her monograph on Markov switching models.

Selected publications
  • Frühwirth-Schnatter, S. (2006). Finite mixture and Markov switching models. Springer Science & Business Media. ISBN 978-0-387-35768-3
  • Conti, G., Frühwirth-Schnatter, S., Heckman, J. J., & Piatek, R. (2014). Bayesian exploratory factor analysis. Journal of econometrics, 183(1), 31–57.
  • Frühwirth‐Schnatter, S. (1994). Data augmentation and dynamic linear models. Journal of time series analysis, 15(2), 183–202.
gollark: I think it's a nice-to-have property but not worth sacrificing much else for.
gollark: You can see when it is *happening*, if you happen to be active, and ignore it for a bit.
gollark: You can just mute them *when* discomforting things happen, or possibly mute <#348702212110680064> if you mostly care about esolangs.
gollark: See, I was halfway through writing about why that wasn't a good solution.
gollark: I think it is somewhat more valuable to be able to have reasonable discussion about controversial topics than to make some people able to not mute things at some points.

References
  1. "Leadership". International Society for Bayesian Analysis. Retrieved February 16, 2019.
  2. "Sylvia Frühwirth-Schnatter". Vienna University of Economics and Business. Retrieved February 16, 2019.
  3. Conti, G.; Frühwirth-Schnatter, S.; Heckman, J. J.; Piatek, R. (November 2014). "Bayesian exploratory factor analysis". Journal of Econometrics. 183 (1): 31–57. doi:10.1016/j.jeconom.2014.06.008. PMC 4242469. PMID 25431517.
  4. "WU Best Paper Award". Vienna University of Economics and Business. Retrieved February 16, 2019.
  5. "DeGroot Prize". International Society for Bayesian Analysis. Retrieved February 16, 2019.
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