Skip to content
OpenTrain AIFor AI Companies

Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference

Nicolás Dalmassó, Taylor Pospisil, A.B. Lee, Rafael Izbicki, Peter E. Freeman +1 morePublished Jan 1, 2020
DOI Publisher
Researcher verdict
Context only
Use as context only
Benchmark evidence
Missing
Not verified yet
Time to first repro
A few days
Plan setup time
Risk flags
1
Review before use

Results and benchmarks

Freshness tier: cold
Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference presents a algorithm approach for computer science.

Implementation

No direct implementation yet

Maintained implementation evidence is not confirmed for this paper yet.

Use the implementation status and reproduction sections for the current action plan.

Implementation evidence summary
Confidence: low

freelunchtheorem/Conditional_Density_Estimation is the closest maintained adjacent implementation (Matches contextual method/domain keyword: density estimation). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 202 GitHub stars.

Reproduction risks
  • Adjacent implementations are not paper-verified
  • Recommended repository is adjacent and not paper-verified.
  • Adjacent implementation match confidence is low.

Reproduction readiness

Time to first repro: days
Last checked: Aug 19, 2026

No repo

No verified implementation available

  • No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.

Hardware requirements

  • Expect multi-day setup/compute for meaningful reproduction based on current guidance.

Repositories and ecosystem

Closest related implementations

These are not paper-verified. Use them as reference points when no direct implementation is available.

No additional verified repositories beyond the primary recommendation.

Hugging Face artifacts

No trustworthy direct or curated related Hugging Face artifacts were found yet. Use targeted searches to quickly locate candidate models, datasets, and demos.

Tip: start with models, then check datasets and spaces if you need evaluation data or demos.

Research context

44

Citations

129

References

Tasks

Computer science, Python (programming language), Inference, Nonparametric statistics, Density estimation, Redshift, Conditional probability distribution, Probability density function

Methods

Algorithm

Domains

Machine learning, Artificial intelligence, Astrophysics, Physics

Evaluation and human feedback data

Open this paper in HFEPX to review benchmark signals, evaluation modes, and human-feedback protocol context.

Open in HFEPX
Explore similar papers