I'm confused with the notation for estimators in Chapter 6 of Murphy's "Machine Learning: A Probabilistic Perspective":
In section 6.2, he initially refers to an estimator as $\delta$ in the beginning of the section.
...is computed by applying an estimator $\delta$ to some data D, so $\hat{\theta} = \delta(D)$.
Later in that paragraph, he refers to the estimator as the $\hat{\theta}(\cdot)$ function.
Now apply the estimator $\hat{\theta}(\cdot)$ to each D...
Then in section 6.2.1, he says the estimator is equal to a function f().
We could then compute our estimator from each sample, $\hat{\theta^s} = f(x_{1:N}^s)$ and...
My question is why are there three different notations for the estimator? Are they all the same?