How is Bayesian Estimation related to filtering? I am reading about estimation theory, including topics like Bayesian Estimation (e.g. Wiener Filtering).

It seems that we usually define a filter in terms of tis frequency response (e.g. High Pass, Low Pass). On the other hand, Wiener Filter works by filtering out noise without specific reference to frequency domain.

How are the two related? If not, are they just two different approaches to the filtering problem?
In Wiener filtering, you filter a noisy signal to more closely resemble a desired signal that you have access to.  In Bayesian estimation, you take prior knowledge into account to estimate some state given noisy measurements.  In frequency filtering, you just remove frequency content from a signal.