# Hidden Markov Models vs. Markov Models

## What's the Difference?

Hidden Markov Models (HMMs) and Markov Models are both types of probabilistic models used in machine learning and statistics. The main difference between the two lies in the visibility of the states. In Markov Models, the states are directly observable, while in HMMs, the states are hidden and can only be inferred through the observed data. HMMs are often used in speech recognition, bioinformatics, and natural language processing, where the underlying states are not directly observable. Markov Models, on the other hand, are commonly used in modeling sequential data where the states are known and can be directly observed.

## Comparison

Attribute | Hidden Markov Models | Markov Models |
---|---|---|

Observations | Observations are not directly visible | Observations are directly visible |

States | States are hidden | States are visible |

Transition Probabilities | Transition probabilities between hidden states | Transition probabilities between visible states |

Emission Probabilities | Emission probabilities for observations given hidden states | N/A |

Applications | Speech recognition, bioinformatics, etc. | Text prediction, weather forecasting, etc. |

## Further Detail

### Introduction

Hidden Markov Models (HMMs) and Markov Models are both powerful tools used in various fields such as speech recognition, bioinformatics, and natural language processing. While they share similarities in terms of their underlying principles, they also have distinct attributes that set them apart. In this article, we will explore the key differences between HMMs and Markov Models, highlighting their strengths and weaknesses.

### Definition and Basic Concepts

Markov Models are a type of stochastic model that assumes the probability of a future state depends only on the current state and not on the sequence of events that preceded it. This property is known as the Markov property. In contrast, Hidden Markov Models extend the concept of Markov Models by introducing hidden states that generate observable outputs. These hidden states are not directly observable, making HMMs a type of probabilistic model that deals with both observed and hidden variables.

### State Representation

In a Markov Model, the system is represented by a finite set of states, each with a probability distribution over possible transitions to other states. The transitions between states are determined by transition probabilities, which specify the likelihood of moving from one state to another. In an HMM, there are two sets of states: hidden states and observable states. The hidden states are not directly observable, while the observable states emit outputs based on their corresponding emission probabilities.

### Applications

Markov Models are commonly used in various applications such as natural language processing, where they are used for tasks like part-of-speech tagging and text generation. They are also used in speech recognition systems to model the sequential nature of speech signals. On the other hand, Hidden Markov Models are widely used in speech recognition, bioinformatics, and gesture recognition. HMMs are particularly well-suited for tasks that involve sequential data and where the underlying states are not directly observable.

### Training and Inference

Training a Markov Model involves estimating the transition probabilities between states based on observed data. This can be done using techniques such as maximum likelihood estimation or the Baum-Welch algorithm. Inference in a Markov Model involves predicting future states or sequences of states based on the model's parameters. In contrast, training an HMM involves estimating both the transition probabilities between hidden states and the emission probabilities of observable states. This is typically done using the Baum-Welch algorithm. Inference in an HMM involves determining the most likely sequence of hidden states given a sequence of observations, which can be done using the Viterbi algorithm.

### Complexity and Scalability

Markov Models are relatively simple and easy to implement, making them suitable for tasks that involve a small number of states and transitions. However, as the number of states and transitions increases, the complexity of the model grows, which can make it challenging to train and infer accurately. On the other hand, Hidden Markov Models are more complex than Markov Models due to the presence of hidden states and emission probabilities. This complexity can make training and inference more computationally intensive, especially for large datasets. However, HMMs are still widely used in practice due to their ability to model complex sequential data.

### Conclusion

In conclusion, Hidden Markov Models and Markov Models are both valuable tools for modeling sequential data and making predictions based on probabilistic relationships between states. While Markov Models are simpler and easier to implement, Hidden Markov Models offer more flexibility and power by incorporating hidden states and emission probabilities. The choice between the two models depends on the specific requirements of the task at hand, with Markov Models being suitable for simpler tasks and HMMs being more appropriate for complex sequential data. By understanding the strengths and weaknesses of each model, practitioners can make informed decisions when choosing the right tool for their applications.

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