Mathematics Branches, Topics, and Sub-Topics

A structured visual guide to the major mathematical areas and their relationships.

Search by code, branch, topic, subtopic, or a keyword from the descriptions.

62Hxx Multivariate analysis

This subtopic introduces the core ideas in multivariate analysis, including foundational concepts, standard methods, and the main questions used to organize the area. Typical uses include building mathematical background, framing related research problems, and supporting applications in neighboring fields where these concepts provide useful structure.

Specific topics

62H05 Characterization and structure theory for multivariate distributions

Overview

Characterization and structure theory for multivariate distributions. This topic studies multivariate analysis, including dependence structures, high-dimensional summaries, and matrix-based inference.

Related Wikipedia Page

Wikipedia: Multivariate statistics

Useful Links

Key Ideas

  • joint distribution and dependence modeling
  • dimension reduction and latent structure
  • matrix methods for inference

Typical Uses

Used for simultaneous analysis of multiple correlated variables and structure extraction.

Applications

  • Omics and high-dimensional science
  • Finance and portfolio analytics
  • Pattern recognition and classification

References

Recommended Textbooks

62H10 Multivariate distributions and inference

Overview

Multivariate distributions and inference. This topic studies multivariate analysis, including dependence structures, high-dimensional summaries, and matrix-based inference.

Related Wikipedia Page

Wikipedia: Multivariate statistics

Useful Links

Key Ideas

  • joint distribution and dependence modeling
  • dimension reduction and latent structure
  • matrix methods for inference

Typical Uses

Used for simultaneous analysis of multiple correlated variables and structure extraction.

Applications

  • Omics and high-dimensional science
  • Finance and portfolio analytics
  • Pattern recognition and classification

References

Recommended Textbooks

62H11 Directional data; spatial statistics

Overview

Directional data; spatial statistics. This topic studies multivariate analysis, including dependence structures, high-dimensional summaries, and matrix-based inference.

Related Wikipedia Page

Wikipedia: Multivariate statistics

Useful Links

Key Ideas

  • joint distribution and dependence modeling
  • dimension reduction and latent structure
  • matrix methods for inference

Typical Uses

Used for simultaneous analysis of multiple correlated variables and structure extraction.

Applications

  • Omics and high-dimensional science
  • Finance and portfolio analytics
  • Pattern recognition and classification

References

Recommended Textbooks

62H12 Estimation in multivariate analysis

Overview

Estimation in multivariate analysis. This topic studies multivariate analysis, including dependence structures, high-dimensional summaries, and matrix-based inference.

Related Wikipedia Page

Wikipedia: Multivariate statistics

Useful Links

Key Ideas

  • joint distribution and dependence modeling
  • dimension reduction and latent structure
  • matrix methods for inference

Typical Uses

Used for simultaneous analysis of multiple correlated variables and structure extraction.

Applications

  • Omics and high-dimensional science
  • Finance and portfolio analytics
  • Pattern recognition and classification

References

Recommended Textbooks

62H15 Hypothesis testing in multivariate analysis

Overview

Hypothesis testing in multivariate analysis. This topic studies multivariate analysis, including dependence structures, high-dimensional summaries, and matrix-based inference.

Related Wikipedia Page

Wikipedia: Multivariate statistics

Useful Links

Key Ideas

  • joint distribution and dependence modeling
  • dimension reduction and latent structure
  • matrix methods for inference

Typical Uses

Used for simultaneous analysis of multiple correlated variables and structure extraction.

Applications

  • Omics and high-dimensional science
  • Finance and portfolio analytics
  • Pattern recognition and classification

References

Recommended Textbooks

62H17 Contingency tables

Overview

Contingency tables. This topic studies multivariate analysis, including dependence structures, high-dimensional summaries, and matrix-based inference.

Related Wikipedia Page

Wikipedia: Multivariate statistics

Useful Links

Key Ideas

  • joint distribution and dependence modeling
  • dimension reduction and latent structure
  • matrix methods for inference

Typical Uses

Used for simultaneous analysis of multiple correlated variables and structure extraction.

Applications

  • Omics and high-dimensional science
  • Finance and portfolio analytics
  • Pattern recognition and classification

References

Recommended Textbooks

62H20 Measures of association (correlation, canonical correlation, etc.)

Overview

Measures of association (correlation, canonical correlation, etc.). This topic studies multivariate analysis, including dependence structures, high-dimensional summaries, and matrix-based inference.

Related Wikipedia Page

Wikipedia: Multivariate statistics

Useful Links

Key Ideas

  • joint distribution and dependence modeling
  • dimension reduction and latent structure
  • matrix methods for inference

Typical Uses

Used for simultaneous analysis of multiple correlated variables and structure extraction.

Applications

  • Omics and high-dimensional science
  • Finance and portfolio analytics
  • Pattern recognition and classification

References

Recommended Textbooks

62H22 Probabilistic graphical models

Overview

Probabilistic graphical models. This topic studies multivariate analysis, including dependence structures, high-dimensional summaries, and matrix-based inference.

Related Wikipedia Page

Wikipedia: Multivariate statistics

Useful Links

Key Ideas

  • joint distribution and dependence modeling
  • dimension reduction and latent structure
  • matrix methods for inference

Typical Uses

Used for simultaneous analysis of multiple correlated variables and structure extraction.

Applications

  • Omics and high-dimensional science
  • Finance and portfolio analytics
  • Pattern recognition and classification

References

Recommended Textbooks

62H25 Factor analysis and principal components; correspondence analysis

Overview

Factor analysis and principal components; correspondence analysis. This topic studies multivariate analysis, including dependence structures, high-dimensional summaries, and matrix-based inference.

Related Wikipedia Page

Wikipedia: Multivariate statistics

Useful Links

Key Ideas

  • joint distribution and dependence modeling
  • dimension reduction and latent structure
  • matrix methods for inference

Typical Uses

Used for simultaneous analysis of multiple correlated variables and structure extraction.

Applications

  • Omics and high-dimensional science
  • Finance and portfolio analytics
  • Pattern recognition and classification

References

Recommended Textbooks

62H30 Classification and discrimination; cluster analysis

Overview

Classification and discrimination; cluster analysis. This topic studies multivariate analysis, including dependence structures, high-dimensional summaries, and matrix-based inference.

Related Wikipedia Page

Wikipedia: Multivariate statistics

Useful Links

Key Ideas

  • joint distribution and dependence modeling
  • dimension reduction and latent structure
  • matrix methods for inference

Typical Uses

Used for simultaneous analysis of multiple correlated variables and structure extraction.

Applications

  • Omics and high-dimensional science
  • Finance and portfolio analytics
  • Pattern recognition and classification

References

Recommended Textbooks

62H35 Image analysis in multivariate analysis

Overview

Image analysis in multivariate analysis. This topic studies multivariate analysis, including dependence structures, high-dimensional summaries, and matrix-based inference.

Related Wikipedia Page

Wikipedia: Multivariate statistics

Useful Links

Key Ideas

  • joint distribution and dependence modeling
  • dimension reduction and latent structure
  • matrix methods for inference

Typical Uses

Used for simultaneous analysis of multiple correlated variables and structure extraction.

Applications

  • Omics and high-dimensional science
  • Finance and portfolio analytics
  • Pattern recognition and classification

References

Recommended Textbooks