HIDDEN MARKOV MODELS (HMM)

Hidden Markov models (HMMs) are generative models that simulate a series of events. They are based on a prior distribution of hidden states and observations. The model also incorporates emission probabilities, which are the probabilities that a given state will emit an observed signal. In these algorithms, a uniform prior distribution is implicitly assumed, but other prior distributions are also possible. The Dirichlet distribution, for example, is a conjugate prior distribution that reflects ignorance about which states are inherently more likely to occur.
HMMs have several important features. First, they allow the creation of complicated models. These models are very useful for applications in artificial intelligence and machine learning. For example, an HMM can simulate a series of events that occur in a certain sequence.
The second feature of Hidden Markov models is that they can be tunable. This means that you can control which hidden states are observed. You can do this by using optional parameters. These parameters include the inertia of the distributions and the use of pseudocounts. The hidden states are also characterized by labels that describe their probability of transition.
Moreover, HMMs are easy to construct. The state structure of each HMM is built up of a series of sub-models, with each sub-model containing only the features of the training set. In addition, they are widely used in digital communication and speech recognition. In fact, the success of HMMs in these fields has made them popular among scientists and engineers.