  Contamination by Radio Frequency Interference (RFI) is a ubiquitous challenge
for radio astronomy. In particular, transient RFI is difficult to detect and
avoid, especially in large data sets with many time bins. In this work, we
present a Bayesian methodology for time-dependent, transient anomaly
mitigation. In general, the computation time for correcting for transient
anomalies in time-separated data sets grows proportionally with the number of
time bins. We demonstrate that utilising likelihood reweighting can allow our
Bayesian anomaly mitigation method to be performed with a computation time
close to independent of the number of time bins. In particular, we identify a
factor of 25 improvement in computation time for a test case with 2000 time
bins. We also demonstrate how this method enables the flagging threshold to be
fit for as a free parameter, fully automating the mitigation process. We find
that this threshold fitting also prevents overcorrecting of the data in the
case of wide priors. Finally, we investigate the potential of the methodology
as a transient detector. We demonstrate that the method is able to reliably
flag an individual anomalous data point out of 302,000 provided the SNR > 10.
