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Implementation of likelihoods for cluster abundance cosmology

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
30/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
jupyter-notebook, python
Domain
analytics, data

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

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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