than the last. Here “narrower” is to be understood as a multivariate Gaussian proposal density, on which the adaptive-Metropolis algorithm is based, that has a smaller variance for at least one of the parameters in the parameter vector. In short, if a value proposed by drawing it from an initial proposal density is rejected, we continue by modifying the proposal density with respect to the period parameter(s) by multiplying it with a factor of 0.1 such that another value is proposed from a narrower area surrounding the current state of the chain. This enables us to visit the areas of high probability in the period space repeatedly and reliably and to see which periods correspond to the highest values of the posterior probability density.

We applied a statistical model that contains reference velocities of each instrument, a linear trend, Keplerian signals, and excess white noise. By simplifying the statistical model in @tuomi14b by removing the intrinsic correlations that we do not expect to play a significant role due to the low number of measurements and the (relatively) high amplitude variations in the data, the model can be written as

$$\label{eq:rv_model}
m_{i,l} = \gamma_{l} + \dot{\gamma}t_{i} + f_{k}(t_{i}) + \epsilon_{i,l}$$

where 