I kicked off day 2 of the Cosmic Dawn meeting with a discussion of how Bayesian inference and marginalization of nuisances can be used to improve astronomical inferences with spectroscopy. It turned out I was preaching to the converted, because the next five or six talks all fully endorsed Bayesian approaches. This is a big change in our community. Along those lines, Brinchmann emphasized that Bayesian approaches are a good idea because they implicitly encourage investigators to make their decisions and assumptions explicit. I agree, although there is really nothing about frequentism that prevents this! Stenning showed a very nice, very simple, very good hierarchical inference of star-formation relationships among noisily observed galaxies; that warmed my heart.
Leja showed (reinforcing much vaguer content from my talk) that different models give different answers about galaxies even given the same data. He also showed that in general, inferred stellar masses and star-formation histories are inconsistent, which is potentially bad. He showed nice results that they can use photometry (plus serious, complex modeling) to predict spectroscopic indices (like line strengths). This is a great way to validate the models, because it makes comparisons in the space of observables, not latents. I talked to him and Ben Johnson afterwards about generalizing this idea for experimental design.
There was a lot of discussion (from Heckman, Erb, Henry) of the problem of understanding the Lyman-alpha luminosity density of the Universe, with all of them to some extent wondering whether we could predict Lyman-alpha emission from other (easier to observe) emission lines. That seems like an interesting project for data-driven approaches.