HMMS is an acronym that stands for Hidden Markov Models (HMMs). HMMs are statistical models widely used in fields such as computer science, machine learning, and speech recognition. An HMM is a type of probabilistic model that works with sequences of observations or events.
The term "hidden" refers to the underlying state of a system that generates the sequence of observations. Each state is associated with a particular probability distribution of possible observations. However, as the name suggests, these states are not directly observable. Instead, only the sequence of observations generated by the underlying states is observable.
HMMS consist of a set of states, a set of observation symbols, initial state probabilities, state transition probabilities, and emission probabilities. The states form a chain-like structure in which each state is connected to other states through transition probabilities. These probabilities determine the likelihood of transitioning from one state to another at each time step.
The main goal of HMMS is to estimate the most likely sequence of hidden states given a sequence of observations. This can be achieved through the use of algorithms such as the Viterbi algorithm or the Baum-Welch algorithm.
HMMS find applications in a variety of fields, including speech recognition, natural language processing, bioinformatics, and robotics. By modeling the underlying state dynamics, HMMS enable the generation of accurate predictions and classifications in sequential data analysis.
