Since \( \widehat{\theta} = \widehat{\theta}(\hat{X}) \) is a function of random variables, \( \widehat{\theta} \) itself must be a random variable. Thus it has a pdf, call this function \( p(\hat{t}) \). The aim of the bootstrap is to estimate \( p(\hat{t}) \) by the relative frequency of \( \widehat{\theta} \). You can think of this as using a histogram in the place of \( p(\hat{t}) \). If the relative frequency closely resembles \( p(\vec{t}) \), then using numerics, it is straight forward to estimate all the interesting parameters of \( p(\hat{t}) \) using point estimators.