The BCJR algorithm is an algorithm for maximum a posteriori decoding of error correcting codes defined on trellises (principally convolutional codes). The algorithm is named after its inventors: Bahl, Cocke, Jelinek and Raviv. This algorithm is critical to modern iteratively-decoded error-correcting codes, including turbo codes and low-density parity-check codes. Steps involved Based on the trellis: Compute forward probabilities Compute backward probabilities Compute smoothed probabilities based on other information (i.e. noise variance for AWGN, bit crossover probability for binary symmetric channel) Variations SBGT BCJR Berrou, Glavieux and Thitimajshima simplification. Log-Map BCJR Implementations Susa framework implements BCJR algorithm for forward error correction codes and channel equalization in C++. See also Forward-backward algorithm Maximum a posteriori (MAP) estimation Hidden Markov model References External links The online textbook: Information Theory, Inference, and Learning Algorithms, by David J.C. MacKay, discusses the BCJR algorithm in chapter 25. The implementation of BCJR algorithm in Susa signal processing framework Error detection and correction