Eikosstatistics & data science

Statistical Modeling & Regression · Multivariate Analysis

Bayesian canonical correlation analysis (BCCA) — foundations and probabilistic approach

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Introduction Bayesian Statistics · Inference in Latent Spaces

Overview

Topics covered

  • Introduction
  • Bayesian Canonical Correlation Analysis
  • Fundamentals of Classical Canonical Correlation Analysis (CCA)
  • The Transition to the Probabilistic Perspective (PCCA)
  • The Bayesian Canonical Correlation Analysis (BCCA) Model
  • Priori Distributions (Priors) in BCCA and Automatic Regularization

Related concepts

Main connections

  • Bayes' theorem & priors
  • Markov chain Monte Carlo (MCMC)
  • Linear algebra & matrices
  • Variance & standard deviation
  • Missing data & multiple imputation
  • Maximum likelihood estimation

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

Σ=Cov(XY)=(ΣxxΣxyΣyxΣyy)
View LaTeX source\Sigma = \text{Cov} \begin{pmatrix} X \\ Y \end{pmatrix} = \begin{pmatrix} \Sigma_{xx} & \Sigma_{xy} \\ \Sigma_{yx} & \Sigma_{yy} \end{pmatrix}