Lattice-based cryptography is in the process of being standardized. Several proposals to deal with side-channel information using lattice reduction exist. However, it has been shown that algorithms based on Bayesian updating are often more favorable in practice. In this work, we define distribution hints; a type of hint that allows modelling probabilistic information. These hints generalize most previously defined hints and the information obtained in several attacks. We define two solvers for our hints; one is based on belief propagation and the other one uses a greedy approach. We prove that the latter is a computationally less expensive approximation of the former and that previous algorithms used for specific attacks may be seen as special cases of our solvers. Thereby, we provide a systematization of previously obtained information and used algorithms in real-world side-channel attacks. In contrast to lattice-based approaches, our framework is not limited to value leakage. For example, it can deal with noisy Hamming weight leakage or partially incorrect information. Moreover, it improves upon the recovery of the secret key from approximate hints in the form they arise in real-world attacks. Our framework has several practical applications: We exemplarily show that a recent attack can be improved; we reduce the number of traces and corresponding ciphertexts and increase the noise resistance. Further, we explain how distribution hints could be applied in the context of previous attacks and outline a potential new attack.