Symbol of precision and precision matrix

This is one of my first questions so sorry in advance for any rules that I break.

Is there any mainstream convention for the symbol of the precision and precision matrix?

I have seen the following ones:

• In Information theory, inference and learning algorithms by MacKay:

$$\text{Precision: }\tau = 1 / \sigma^2$$ $$\text{Precision matrix: }\mathbf{A} = \boldsymbol{\Sigma}^{-1}$$

• In Pattern recognition and machine learning by Bishop:

$$\text{Precision: }\beta = 1 / \sigma^2$$ $$\text{Precision matrix: }\boldsymbol{\Lambda} = \boldsymbol{\Sigma}^{-1}$$

Is any of them preferred over the others?

I change a lot but in the Gaussian Process literature, it's usually $$K$$ (for kernel matrix) or $$\Sigma$$. Precision matrices also change a lot but as you say $$A$$ and $$\Lambda$$ are pretty common. As long as you clearly introduce the term though you can use whichever convention you want.