all have to be accounted for in a search for periodic signals. The signals we discuss in this work are a mixture of well, and moderately well sampled data, such that some signals are more problematic to detect than others. Moreover, the inclusion of a linear trend that could be present in the data of a given target due to a long period companion, will cause considerable correlations to the probability densities of the model parameters.

In Appendix \[appendix0\] we show the MMSE power spectra for our sample of stars and it is clear that a number of deep troughs are present in the data, yet some sets show no significant power troughs over the frequency space searched. We thus applied the delayed-rejection adaptive-Metropolis algorithm (DRAM; [@haario06]) together with tempered Markov chains when searching for solutions to our Keplerian models (see [@tuomi14a; @tuomi14b]) and the simpler adaptive-Metropolis algorithm ([@haario01]) when obtaining estimates for the model parameters. This method has previously been applied in @tuomi13a, @jenkins13a, @jenkins13c, and @jenkins14b, for example. The vertical dashed lines in the MMSE periodograms mark the positions of these detected signals.

The DRAM algorithm works by using a sequence of proposal densities that are each narrower 