Series
Introduction to Bayesian Inference (part 2/6)

Tom Loredo's research combines statistical data analysis with astrophysical theory to rigorously test astronomical models and theories, particularly in the areas of high energy astrophysics (supernovae, gamma-ray bursts, black holes, neutron stars) and cosmology. His work focuses on problems that can benefit from development of new statistical methodology, and largely adopts the Bayesian approach to statistics, an approach first developed by the French physicist Laplace but largely abandoned until recently. He also uses the conventional "frequentist" approach, and is particularly interested in problems where these two statistical approaches produce different results. Recently Tom's work has also addressed statistical issues arising in the study of extrasolar planets and analysis of the distribution of trans-Neptunian objects (including Kuiper belt objects).

Tom has been the principal investigator for a NASA-sponsored project developing a statistical inference package using the Python computing language. He is also a member of the Extrasolar Planet Interferometric Survey (EPIcS) team that will use NASA's Space Interferometry Mission to search for Earth-like planets around nearby stars and to take a census of planetary systems in the Solar neighborhood in all their diversity.

Tom is affiliated with both the Center for Astrostatistics at PSU, and the Statistical and Applied Mathematical Sciences Institute (SAMSI). With both institutions he has helped organize research and educational programs for the emerging discipline of astrostatistics.

For more details about Tom's Bayesian astrostatistical work, visit Tom Loredo's Bayesian Reprints, or download Tom's CV for a more complete publication list. For information about his other activities and interests, visit the following web sites he maintains:

• Scientific Resources:
  • BIPS: Bayesian Inference for the Physical Sciences
  • From Unix to Mac OS X (2007)
  • Python Statistical Computing Essentials (Jan 2006; the subsequent release of Numpy 1.0 has simplified things)
  • StatPy: Statistical Computing With Python (partly out-of-date)

   

• Other Resources:
  • Tom Loredo's Bookmarks
  • More about Tom Loredo