Baum-Welch algorithm

The Baum-Welch algorithm is a method used to estimate the unknown parameters of a Hidden Markov Model (HMM). HMMs are models that analyze systems with hidden states influencing observable data, like speech or stock prices. The algorithm works by repeatedly adjusting the model's parameters—such as the likelihood of moving between states or producing certain observations—to better fit the observed data. It uses an iterative process, calculating the probability of different state sequences and refining the parameters until the model accurately reflects the patterns in the data. This helps in inferring the most likely hidden states and dynamics behind observed sequences.