You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: The AEVB algorithm exposes a connection between directed probabilistic models (trained with a variational objective) and auto-encoders. A connection between linear auto-encoders and a certain class of generative linear-Gaussian models has long been known. In  [Row98] it was shown that PCA corresponds to the maximum-likelihood (ML) solution of a special case of the linear-Gaussian model with a prior p(\mathbf{z})=\mathcal{N}(0,\mathbf{I}) and a conditional distribution p(\mathbf{x}|\mathbf{z})=\mathcal{N}(\mathbf{x};\mathbf{W}\mathbf{z},\epsilon\mathbf{I}), specifically the case with infinitesimally small \epsilon.

Question: According to authors, there’s a connection between PCA and maximum-likelihood (ML) solution of a special case of the linear-Gaussian model. Is this true?
Yes, it is. In  [Row98] it was shown that PCA corresponds to the maximum-likelihood (ML) solution of a special case of the linear-Gaussian model with a prior p(\mathbf{z})=\mathcal{N}(0,\mathbf{I}) and a conditional distribution p(\mathbf{x}|\mathbf{z})=\mathcal{N}(\mathbf{x};\mathbf{W}\mathbf{z},\epsilon\mathbf{I}), specifically the case with infinitesimally small \epsilon.