  Extracting Times of Arrival from pulsar radio signals depends on the
knowledge of the pulsars pulse profile and how this template is generated. We
examine pulsar template generation with Bayesian methods. We will contrast the
classical generation mechanism of averaging intensity profiles with a new
approach based on Bayesian inference. We introduce the Bayesian measurement
model imposed and derive the algorithm to reconstruct a "statistical template"
out of noisy data. The properties of these "statistical templates" are analysed
with simulated and real measurement data from PSR B1133+16. We explain how to
put this new form of template to use in analysing secondary parameters of
interest and give various examples: We implement a nonlinear filter for
determining ToAs of pulsars. Applying this method to data from PSR J1713+0747
we derive ToAs self consistently, meaning all epochs were timed and we used the
same epochs for template generation. While the average template contains
fluctuations and noise as unavoidable artifacts, we find that the "statistical
template" derived by Bayesian inference quantifies fluctuations and remaining
uncertainty. This is why the algorithm suggested turns out to reconstruct
templates of statistical significance from ten to fifty single pulses. A moving
data window of fifty pulses, taking out one single pulse at the beginning and
adding one at the end of the window unravels the characteristics of the methods
to be compared. It shows that the change induced in the classical
reconstruction is dominated by random fluctuations for the average template,
while statistically significant changes drive the dynamics of the proposed
method's reconstruction. The analysis of phase shifts with simulated data
reveals that the proposed nonlinear algorithm is able to reconstruct correct
phase information along with an acceptable estimation of the remaining
uncertainty.
