Editing Baum–Welch algorithm (section)

{{Short description|Algorithm in mathematics}}
In [[electrical engineering]], [[statistical computing]] and [[bioinformatics]], the '''Baum–Welch algorithm''' is a special case of the [[expectation–maximization algorithm]] used to find the unknown parameters of a [[hidden Markov model]] (HMM). It makes use of the [[forward-backward algorithm]] to compute the statistics for the expectation step. The Baum–Welch algorithm, the primary method for inference in hidden Markov models, is numerically unstable due to its recursive calculation of joint probabilities. As the number of variables grows, these joint probabilities become increasingly small, leading to the forward recursions rapidly approaching values below machine precision.<ref>{{Cite web |title=Scaling Factors for Hidden Markov Models |url=https://gregorygundersen.com/blog/2022/08/13/hmm-scaling-factors/ |access-date=2024-10-19 |website=gregorygundersen.com}}</ref>