The paper ‘Bayesian time series analysis of terrestrial impact cratering’ was published in Mon. Not. R. Astron. Soc. **416**, 1163–1180 (2011). There is an error in the write-up of the method in sections 3.2 and 3.4 of the article. It is essentially just an error in the notation used, but this may interfere with a complete understanding of the method. There is no mistake in the concept or in the code, so this does not affect the results, discussion or conclusions in any way.

The issue is with equation (5) (section 3.2, Bailer-Jones 2011), the equation for the measurement model. Roughly speaking the measurement model gives the probability of getting the measured data given the true value. More precisely, it is the probability of measuring the event at time given both the true time of the event, , *and* the uncertainty, , in the measurement . In other words, rather than being , the measurement model is

This changes the notation in section 3.4 of Bailer-Jones (2011), but not the actual content of the equations or the calculations. One can just replace with and remember that there is an implicit conditioning on . None the less, for completeness, the second, third and fourth paragraphs of this section with the corrected notation are given below.

The consequence of this more precise notation is that it reminds us that the evidence, , is actually conditioned on the fixed measurement uncertainties. This was not explicit in the original article, but the results, conclusions and discussion are entirely unaffected.

## AMENDED PARAGRAPHS OF SECTION 3.4 (BAILER-JONES 2011)

…The probability of observing data from model with parameters is , the *likelihood* for one event. The time series model predicts the true age of an event, which is unknown. Applying the rules of probability we marginalize over this to get

If we have a set of events for which the ages and uncertainties have been estimated independently of one another, then the probability of observing these data , the *likelihood*, is

The *evidence* is obtained by marginalizing the likelihood over the parameter prior probability distribution, ,