BaumWelchalgoritmen
The Baum-Welch algorithm is a dynamic programming algorithm used for training Hidden Markov Models (HMMs). It is an expectation-maximization (EM) algorithm that iteratively refines the parameters of an HMM to maximize the probability of observing a given sequence of data. The algorithm was developed by Leonard E. Baum and his colleagues.
The core idea of the Baum-Welch algorithm is to estimate the hidden state sequence given the observed
The algorithm is guaranteed to converge to a local maximum of the likelihood function. It is widely