jeff-regier / Celeste.jl

Scalable inference for a generative model of astronomical images
MIT License
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mcmc inference #714

Closed andymiller closed 6 years ago

andymiller commented 6 years ago

added support for MCMC inference

The following call should return a list of res objects (dictionaries)

 res_list = ParallelRun.one_node_single_infer(
     catalog_entries, target_sources, neighbor_map, images;
     config=Config(25.0), do_vi=false)

each object is a dictionary, with the following entries

The list of dictionaries above can be consolidated into a one-source-per-row dataframe with approximate posterior means and stderr values with the following call

star_summary, gal_summary, joint_summary = MCMC.consolidate_samples(res_list)

Here, star_summary assumes all sources are stars, gal_summary assumes all sources are galaxies, and joint_summary presents star posterior inferences if exp(ave_pstar) > .5, and galaxy posterior inferences otherwise.

Closes #387 . Closes #714 . Closes #365 .

jeff-regier commented 6 years ago

Hi @andy Will you rebase on master, or merge master into mcmc-inference again? Looks like a lot of stuff ended up in this PR that was in previous merges. (The first commit here is from 11 months ago).

jeff-regier commented 6 years ago

process_source_mcmc looks good. It's hard to parse the rest because there's a lot in the diff that I think was already committed. Everything through removed stale NPZ reference was part of PR #681