Modern astronomy involves complex data generating mechanisms, complex data collection mechanisms, and complex underlying physics questions, resulting in an abundance of complex statistical challenges. In particular, astronomers may rely on computer simulators to model complex physics, creating a need for statistical methodology that combines these simulators with astrophysical data to perform inference. In this talk I will describe my current work in astrostatistics, which involves developing statistical methods that incorporate physics-based computer simulators, are suited to the particular scientific and data-analytic challenges at hand, and provide uncertainty quantification. Areas of application within astronomy include exoplanet detection, solar physics, stellar evolution, and Mars planetary science, among others.
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2020-11-30T15:00:002020-11-30T16:00:00Computer Simulations and Bayesian Inference in AstrostatisticsEvent Information:
Modern astronomy involves complex data generating mechanisms, complex data collection mechanisms, and complex underlying physics questions, resulting in an abundance of complex statistical challenges. In particular, astronomers may rely on computer simulators to model complex physics, creating a need for statistical methodology that combines these simulators with astrophysical data to perform inference. In this talk I will describe my current work in astrostatistics, which involves developing statistical methods that incorporate physics-based computer simulators, are suited to the particular scientific and data-analytic challenges at hand, and provide uncertainty quantification. Areas of application within astronomy include exoplanet detection, solar physics, stellar evolution, and Mars planetary science, among others.Event Location:
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