Artificial Intelligence Programming 2025 – 400 Free Practice Questions to Pass the Exam

Hidden Markov models (HMMs) are characterized by their ability to represent processes that involve unobserved or hidden states. In an HMM, the underlying system is modeled as a Markov process with hidden states that are not visible to the observer. Instead, the observer can see the outcomes or observations that are generated by these hidden states. The relationship between observed outputs and the hidden states is governed by probabilistic rules, allowing for inference about the hidden states based on observed data.

This framework is particularly powerful in scenarios where direct measurement of the system's state is not possible, yet there are observable indicators that correlate with these states. Applications of HMMs include areas such as speech recognition, bioinformatics, and finance, where the actual underlying processes cannot be directly measured.

The other choices do not accurately capture the essence of hidden Markov models: they are not limited to observed states only, do not rely solely on simple linear equations, and although they are often used for time series data, they are not exclusively designed for this type of application. The core characteristic of HMMs is indeed the presence of hidden states, which makes the chosen answer correct.