**I**n a comment on our *Accelerating Metropolis-Hastings algorithms: Delayed acceptance with prefetching* paper, Philip commented that he had experimented with an alternative splitting technique retaining the right stationary measure: the idea behind his alternative acceleration is again (a) to divide the target into bits and (b) run the acceptance step by parts, towards a major reduction in computing time. The difference with our approach is to represent the overall acceptance probability

and, even more surprisingly than in our case, this representation remains associated with the right (posterior) target!!! Provided the ordering of the terms is random with a symmetric distribution on the permutation. This property can be directly checked via the detailed balance condition*.*

In a toy example, I compared the acceptance rates (*acrat*) for our delayed solution (*letabin.R*), for this alternative (*letamin.R*), and for a non-delayed reference (*letabaz.R*), when considering more and more fractured decompositions of a Bernoulli likelihood.

> system.time(source("letabin.R")) user system elapsed 225.918 0.444 227.200 > acrat [1] 0.3195 0.2424 0.2154 0.1917 0.1305 0.0958 > system.time(source("letamin.R")) user system elapsed 340.677 0.512 345.389 > acrat [1] 0.4045 0.4138 0.4194 0.4003 0.3998 0.4145 > system.time(source("letabaz.R")) user system elapsed 49.271 0.080 49.862 > acrat [1] 0.6078 0.6068 0.6103 0.6086 0.6040 0.6158

A very interesting outcome since the acceptance rate does not change with the number of terms in the decomposition for the alternative delayed acceptance method… Even though it logically takes longer than our solution. However, the drawback is that detailed balance implies picking the order at random, hence loosing on the gain in computing the cheap terms first. If reversibility could be bypassed, then this alternative would definitely get very appealing!