There is one key difference between frequentist statisticians and Bayesian statisticians that we need to know before discussing on Bayesian methods. Frequentists define probability to mean the long-term frequency of an event when an experiment is repeated many times under the same conditions. Bayesians allow probability to a subjective statement about how likely you think an event is to occur. Therefore, frequentists discriminate sharply between a random variable, that can be resampled in every experiment, and a deterministic parameter, that is always the same. Bayesians do not make a sharp distinction between the two.
We will not enter this long standing debate (and fight) between Frequentists and Bayesians with the aim of solving it. The focus of this special session will be on the Bayesian approaches that result to be particularly useful to mitigate or solve detection and estimation problems in radar applications. Bayesian beamforming, matrix estimation, adaptive detection, waveform design and Bayesian bounds are the main topics.
(Bayesian methods for tracking are excluded, since they form a big area per se covered already by many papers).