Maximum Likelihood (ML) and Maximum A Posteriori (MAP) Criteria
MATLAB simulation of Maximum Likelihood (ML) and Maximum A Posteriori (MAP) criteria with algorithm implementation examples
Maximum A Posteriori (MAP) Demodulation for Continuous Phase Modulation (CPM)
MAP Demodulation for Continuous Phase Modulation (CPM) - A statistical probability-based demodulation technique that enhances performance by maximizing posterior probability distributions, with implementation insights including trellis decoding algorithms and Viterbi-based approaches.
Maximum Likelihood (ML) and Maximum A Posteriori (MAP) Criteria
MATLAB Simulation of Maximum Likelihood (ML) and Maximum A Posteriori (MAP) Criteria with Code Implementation Examples
Maximum Likelihood and Maximum A Posteriori Probability Algorithms Description
Detailed explanation of Maximum Likelihood (ML) and Maximum A Posteriori (MAP) probability algorithms, performance comparison, and simulation analysis with code implementation insights
SAR Image Segmentation Method Based on Markov Random Field
This SAR image segmentation approach utilizes Markov Random Field modeling with Maximum a Posteriori probability criterion for target slice segmentation, solved through clustering analysis algorithms. The implementation involves probability distribution modeling and energy minimization using iterative optimization techniques.