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Looking at "Pattern Recognition and Machine Learning" (Bishop, 2006) p28-31, the author appears to be using what would ordinarily be a delimiter for a conditional probability inside a linear function. See the first variable in normpdf as below. This is in the context of defining a Bayesian prior distribution over polynomial coefficients in a curve fitting problem.

[tex]p(\textbf{w} | \alpha) = NormPDF(\textbf{w} | \textbf{0}, \alpha^{-1}\textbf{I}) = \left(\frac{\alpha}{2\pi}\right)^{(M+1)/2} exp \left(-\frac{\alpha}{2}\textbf{w}^T\textbf{w}\right)[/tex]

Can anybody shine some light on this for me please?

Many thanks.

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# Basic notation (conditional probability delim in linear equation)

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