Hidden Markov model

The following are the pros and cons of a Hidden Markov Model when solving sequence-related tasks:

• Pros: Less complex to implement, works faster and as efficiently as RNNs on problems of medium difficulty.
• Cons: HMM becomes exponentially expensive with the desire to increase accuracy. For example, predicting the next word in a sentence may depend on a word from far behind. HMM needs to perform some costly operations to obtain this information. That is the reason why this model is not ideal for complex tasks that require large amounts of data.