Implementation of likelihoods for cluster abundance cosmology
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 30/100
Research direction
Start with modeling/CL_COUNT_class_likelihood.py and the validation work in modeling/Numerical_validation_class_likelihood.ipynb. Compare the four existing likelihood drafts and their tests, then establish what remains for the listed likelihoods; the sample-variance variant is explicitly still under consideration.
Written by the indexing model from the issue text.
Description
Implementation of the various likelihoods for cluster abundance cosmology:
- Binned Gaussian
- Binned Poissonian
- Binned Poissonian & Gaussian mixture
- Unbinned Poissonian
- Unbinned Poissonian with sample variance (still thinking about it)
I made some drafts, the code with the 4 first different likelihoods is here, with tests on likelihoods performed here.
- Dominant language
- Jupyter Notebook
- Stars
- 9
- Forks
- 1
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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