We are organizing a rolling Ask-A-Statistician discussion table during the winter meeting of the American Astronomical Society at Honolulu, HI (Jan 4-8 2020). We will have several statisticians and astrostatisticians present at designated times to discuss several topics with interested astronomers, ranging from the mathematical foundations of Bayesian analysis to Machine Learning.
Schedule | ||
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Day/Time | Topics | People |
Sun Jan 5 1:30-3pm | Statistical inference, Approximate Bayesian Computation, Deep Learning, Machine Learning, non-parametrics, Bayesian parametrics, calibration and systematics | Chad Schafer (Statistics, CMU) Herman Marshall (CXC/MIT) |
Mon Jan 6 1:30-3pm | Bayesian analysis, Bayesian inference, exoplanet detectability, high-dimensional and non-parametric methods | Bo Ning (Statistics, Yale) Gwen Eadie (Astro & Statistics, UToronto) |
Tue Jan 7 3:30-5:30pm | Outlier detection, supervised classification (neural nets, random forests), hierarchical Bayes, Gaussian Linear Models, deconvolution, Ising models | Rafael Martinez-Galarce (CXC/CfA) Katy McKeough (Statistics, Harvard) |
Wed Jan 8 1:30-3:30pm | MCMC, source detection, Type I & II errors, upper limits, Bayesian analysis, calibration and systematics, classification, outliers | Herman Marshall (CXC/MIT) Rafael Martinez-Galarce (CXC/CfA) |
Astronomers attending AAS 235 are welcome to drop in at the Chandra Booth to discuss topics of interest with the statisticians and astrostatisticians present during these times. In order that we may plan for the sessions better, please fill out this google docs sign-up sheet. We ask that you let us know when you plan to stop by the Table, and a brief description of the data analysis issue you want to discuss. We have found through several trials that it often takes multiple iterations before a question can be properly understood and a useful answer formulated.
There are several sessions and talks during AAS 235 that are of interest to astrostatistics and astroinformatics. Here we list those we are aware of. If there is something we have missed, please let us know.