**A** surprising paper came out in the last issue of *Statistical Science*, linking martingales and Bayes factors. In the historical part, the authors (Shafer, Shen, Vereshchagin and Vovk) recall that martingales were popularised by Martin-Löf, who is also influential in the theory of algorithmic randomness. A property of test martingales (i.e., martingales that are non negative with expectation one) is that

which makes their sequential maxima *p*-values of sorts. I had never thought about likelihood ratios this way, but it is true that a (reciprocal) likelihood ratio

is a martingale when the observations are distributed from *p*. The authors define a Bayes factor (for P) as satisfying (Section 3.2)

which I find hard to relate to my understanding of Bayes factors because there is no prior nor parameter involved. I first thought there was a restriction to simple null hypotheses. However, there is a composite versus composite example (Section 8.5, Binomial probability being less than or large than 1/2). So P would then be the marginal likelihood. In this case the test martingale is

Simulating the martingale is straightforward, however I do not recover the picture they obtain (Fig. 6):

x=sample(0:1,10^4,rep=TRUE,prob=c(1-theta,theta))
s=cumsum(x)
ma=pbinom(s,1:10^4,.5,log.p=TRUE)-pbinom(s-1,1:10^4,.5,log.p=TRUE,lower.tail=FALSE)
plot(ma,type="l")
lines(cummin(ma),lty=2) #OR lines(cummin(ma),lty=2)
lines(log(0.1)+0.9*cummin(ma),lty=2,col="steelblue") #OR cummax

**W**hen theta is not 1/2, the sequence goes down almost linearly to -infinity.

but when theta is 1/2, I more often get a picture where max and min are obtained in the first steps:

Obviously, I have not read the paper with the attention it deserved, so there may be features I missed that could be relevant for the Bayesian analysis of the behaviour of Bayes factors. However, at this stage, I fail to see the point of the “Puzzle for Bayesians” (Section 8.6) since the conclusion that “it is legitimate to collect data until a point has been disproven but not legitimate to interpret this data as proof of an alternative hypothesis within the model” is not at odds with a Bayesian interpretation of the test outcome: when the Bayes factor favours a model, it means this model is the most likely of the two given the data, not this model is true.