modality_tests

KO | EN

gitlite — search

#javascript #hacktoberfest #react #rust #deep-learning #docker #macos #nodejs #windows #cli #vue #mcp #ios #openai

(★ 31)

Various python tests in to identify and describe multi-modal data distributions.

modality_tests 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Modality tests and Kernel density estimations

When processing a large number of datasets which can potentially have different data distributions, we are confronted with the following considerations:
- Is the data distribution unimodal and if it is the case, which model best approximates it( uniform distribution, T-distribution, chi-square distribution, cauchy distribution, etc)?
- If the data distribution is multimodal, can we automatically identify the number of modes and provide more granular descriptive statistics?
- How can we estimate the probability density function of a new dataset?

This [notebook](https://github.com/ciortanmadalina/modality_tests/blob/master/kernel_density.ipynb) tackles the following subjects:

- Histograms vs probability density function approximation
- Kernel density estimations
- Choice of optimal bandwidth: Silverman/ Scott/ Grid Search Cross Validation
- Statistical tests for unimodal distributions
- DIP test for unimodality
- Identification of the number of modes of a data distribution based on the kernel density estimation

Find the complete Medium post [here](https://medium.com/@ciortanmadalina/modality-tests-and-kernel-density-estimations-3f349bb9e595)

modality_tests

modality_tests 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

modality_tests

modality_tests 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README