  The angle of Arrival (AoA) estimation is of great interest in modern
communication systems. Traditional maximum likelihood-based iterative
algorithms are sensitive to initialization and cannot be used online. We
propose a Bayesian method to find AoA that is insensitive towards
initialization. The proposed method is less complex and needs fewer computing
resources than traditional deep learning-based methods. It has a faster
convergence than the brute-force methods. Further, a Hedge type solution is
proposed that helps to deploy the method online to handle the situations where
the channel noise and antenna configuration in the receiver change over time.
The proposed method achieves $92\%$ accuracy in a channel of noise variance
$10^{-6}$ with $19.3\%$ of the brute-force method's computation.
