The Markov-Chain-Monte-Carlo method, McMC, adds small random perturbations to the latest model, in favor of searching for models with a high likelihood of fitting the observed data. If the likelihood of a model is already high, in most cases a random perturbation causes a decrease in likelihood. This results in a slow progress of the chain. In this work compensations for these perturbations are introduced in order to increase acceptance rates and step length. These compensations are based on the resolution matrix and are done in slowness. In order to achieve good scaling, different scaling factors of the resolution matrix are tested. Due to computational reasons, all these tests are performed outside a Markov chain. A previous study extracted 500 models from a Markov chain. Although statistic analysis of these models, done as part of this study, reveals strong correlation, this test population is used because of computational reasons. Overall up to 80% of the models can be improved with those compensations. In general, small scaling factors produce a high amount of models with very small improvements. With increasing size of the scaling factor the value of improvements enlarges, but the overall number of enhanced models decreases. Small scaling factors provide a constant result for any resolution of the model parameters. With decreasing resolution compensations with increasing size tend to produce better results. This study acts as a guideline for adding recommended compensations to McMC method.