TheBaum-Welch algorithmis a special case of the Expectation-Maximization (EM) algorithm used to train Hidden Markov Models (HMMs). It estimates model parameters (transition probabilities, emission probabilities) when the training data is incomplete or hidden.
* Viterbi algorithmis for decoding, not training.
* Forward-backward algorithmis part of Baum-Welch's expectation step but is not a standalone training method.
* Exhaustive searchis not a standard HMM training algorithm.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Baum-Welch algorithm iteratively optimizes HMM parameters using forward and backward probability computations until convergence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: HMM Training Algorithms