Providing Quality of Service (QoS) to inelastic data transmissions in a cost-efficient, highly scalable, and realistic fashion in IP networks remains a challenging research issue. In [14], a new approach for a basic, domain-oriented, reactive QoS system based on so-called Load Control Gateways has been proposed and experimentally evaluated. These load control gateways base their load/admission control decisions on observations of simple, binary marking algorithms executed at internal nodes, which allows the gateways to infer knowledge about the load on each path to peer load control gateways. The original load control system proposal utilizes rather simple, conservative admission control decision criteria. In this paper, we focus on methods to improve the admission control decision by using probability theoretical insights in order to better estimate the load situation of a bottleneck on a given path. This is achieved by making assumptions on the probability distribution of the load state of the nodes and analyzing the effect on the path marking probability. We show that even with benevolent assumptions the exact calculation is mathematically intractable for a larger number of internal nodes and develop a heuristic in the form of a Monte Carlo based algorithm. To illustrate the overall benefit of our approach we give a number of numerical examples which provide a quantitative feeling on how the admission control decision can be improved. Overall, we believe the result of this paper to be an important enhancement of the admission control part of the original load control system which allows to make better usage of resources while at the same time controlling statistically the guarantees provided to inelastic transmissions.