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Recently, recursive unwrapping methods such as maximizing a posteriori probability or adopting the expectation-maximization (EM) using the probability model of the observed phase data set are introduced [26, 27].
The model is partly derived by using the so-called maximizing a posteriori (MAP) estimation method.
The unknown parameters of FDMM are computed by maximizing a posteriori (MAP) estimation.
Unlike most methods proposed before, we adopt a generative model and utilize the reconstruction process by maximizing a posteriori estimation (MAP) through Monte Carlo methods.
However, data detection is obtained by an extra ML estimator and a maximizing a posteriori probabilities (APP) detector in [14, 15], respectively.
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In order to maximize a posteriori probability detection, (15) requires determining an optimum decision threshold to meet (16).
This algorithm, which is a 1D algorithm (or namely 1D SLIM), is a maximum a posteriori (MAP) estimator which maximizes a posteriori Bayesian model.
These three different ensembles are one maximizing maximum likelihood hypothesis (MLE), one maximizing maximum a posteriori hypothesis (MAP), a hybrid one maximizing both MLE and MAP hypotheses.
The other strategy, called the "jointly optimum detection," maximizes the a posteriori probability.
The decoding stage, i.e., determining the most probable state sequence given an observation, is performed with the help of Viterbi decoding procedure [31] which maximizes the a posteriori probability: {mathcal{S}}^{opt}=mathit{arg} {max}_{mathcal{S}}left( logPleft(mathcal{S}|{O}^{opt}right)right) (18).
The method uses a Bayesian formulation, and seeks to maximize the a posteriori estimate of the consensus map assembled from the Rmaps.
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