(eng): The contribution studies the problem of collaborative Kalman filtering over distributed networks with or without a fusion center from the theoretically consistent Bayesian perspective. After presenting the Bayesian derivation of the basic Kalman filter, we develop a versatile method allowing exchange of observations among the network nodes and their local incorporation. A probabilistic nodes selection technique based on prior knowledge of nodes performance is proposed to reduce the communication requirements.