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Abstract:
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Scientists often model complex physics using computer simulations. Such simulations complicate statistical inference because the resulting likelihood function cannot be directly evaluated and a single simulation run may take minutes to hours on supercomputers. One example from astrophysics is in the area of stellar evolution, whereby computer simulators are used to predict the brightness of a star in several wide wavelength bands given a set of parameters that describe physical properties of the star (e.g., age, chemical composition, distance from Earth, etc.). Another example comes from simulating plasmas generated by Laser-Induced Breakdown Spectroscopy, a technique used by the ChemCam instrument on the Mars Science Laboratory rover Curiosity, to aid in determining the composition of rocks and soils on Mars. This talk will address the novel statistical challenges that arise when combining such simulations with observational or experimental data for inference, using examples from recent astrostatistical analyses.
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