# Factor Analysis Very similar to probabilistic PCA, just the conditional distribution is different: $ p(\mathbf{x|z} = \mathcal{N}(\mathbf{x|Wz}+\boldsymbol{\mu},\boldsymbol{\Psi}) $ where $ \mathbf{x}= \mathbf{Wz}+\boldsymbol{\mu}+\boldsymbol{\epsilon}\quad \boldsymbol{\epsilon}~\mathcal{N}(\mathbf{z}|0,\boldsymbol{\Psi}) $ - ***W*** - factor loading matrix, explains covariance between observed variables - ***Ψ*** - uniqueness, independent noise variances for each observed variable