Harvard University - Department of Statistics
Many modern statistical applications involve noisy observations of an underlying process that can best be described by a complex deterministic system. In fields such as astronomy, astrophysics and the environmental sciences, these systems often involve the solution of partial differential equations that represent the best available understanding of the physical processes. Statistical computation in this context is typically hampered by either look-up tables or expensive â€œblack-boxâ€ function evaluations.
We present an example from astrophysics with a â€œlook-up table likelihoodâ€: the analysis of stellar populations. Astrophysicists have developed sophisticated models describing how intrinsic physical properties of stars relate to observed photometric data. The mapping between the parameters and the data-space cannot be solved analytically and is represented as a series of look-up tables. We present a flexible hierarchical model for analyzing stellar populations. Our computational framework is applicable to many â€œblack-boxâ€ settings, and robust to the structure of the black-box. The performance of various sampling schemes will be presented, together with the results for an Astronomical dataset.
This is joint work with Xiao-Li Meng, Andreas Zezas and Vinay Kashyap.