Local Bayesian fusion approaches aim to reduce high storage and computational costs of Bayesian fusion which is separated from fixed modeling assumptions. Using the small world formalism, we argue why this proceeding is conform with Bayesian theory. Then, we concentrate on the realization of local Bayesian fusion by focussing the fusion process solely on local regions that are task relevant with a high probability. The resulting local models correspond then to restricted versions of the original one. In a previous publication, we used bounds for the probability of misleading evidence to show the validity of the pre-evaluation of task specific knowledge and prior information which we perform to build local models. In this paper, we prove the validity of this proceeding using information theoretic arguments. For additional efficiency, local Bayesian fusion can be realized in a distributed manner. Here, several local Bayesian fusion tasks are evaluated and unified after the actual fusion process. For the practical realization of distributed local Bayesian fusion, software agents are predestinated. There is a natural analogy between the resulting agent based architecture and criminal investigations in real life. We show how this analogy can be used to improve the efficiency of distributed local Bayesian fusion additionally. Using a landscape model, we present an experimental study of distributed local Bayesian fusion in the field of reconnaissance, which highlights its high potential

Information fusion is essential for the retrieval of desired information in a sufficiently precise, complete, and robust manner. The Bayesian approach provides a powerful and mathematically funded framework for information fusion. By local Bayesian fusion approaches, the computational complexity of Bayesian fusion gets drastically reduced. This is done by a concentration of the actual fusion task on its probably most task relevant aspects. In this contribution, further research results on a special local Bayesian fusion technique called focussed Bayesian fusion are reported. At focussed Bayesian fusion, the actual Bayesian fusion task gets completely restricted to the probably most relevant parts of the range of values of the Properties of Interest. The practical usefulness of focussed Bayesian fusion is shown by the use of an example from the field of reconnaissance. Within this example, final decisions are based on local significance considerations and consistency arguments. As shown in previous publications, the absolute values of focussed probability statements represent upper bounds for their global values. Now, lower bounds which are obtained from the knowledge about the construction of the focussed Bayesian model are proven additionally. The usefulness of the resulting probability interval scheme is discussed