  Once upon a time, predictions for the accuracy of inference on
gravitational-wave signals relied on computationally inexpensive but often
inaccurate techniques. Recently, the approach has shifted to actual inference
on noisy signals with complex stochastic Bayesian methods, at the expense of
significant computational cost. Here, we argue that it is often possible to
have the best of both worlds: a Bayesian approach that incorporates prior
information and correctly marginalizes over uninteresting parameters, providing
accurate posterior probability distribution functions, but carried out on a
simple grid at a low computational cost, comparable to the inexpensive
predictive techniques.
