# Visualizing Hidden Markov Models

A Hidden Markov Model is in essence a finite state machine. The states of the machine output an observable event with some probability. The states themselves are hidden and transition to each other with some probability.

## Vocabulary



A vocabulary is a set of all possible observations. For example, here is a sequence of observations.



Observations can repeat and need not output every element in the Vocabulary

## Observations



A sequence of observations. Each observation is output by a state. For the above example, and

## Length



A Hidden Markov Model is a sequence of observations and states. The extra states are the start and final states.

## Set of States



is a set of possible states. Special states, and , denote the start and final states respectively.

## Hidden States

A Hidden Markov Model will start with , followed by states and end with . Here is a possible sequence of hidden states for the above example observations

States can repeat and need not include every element in the Set of States

## Indexing

A state/observation in the sequence of states/observations is indexed by . Thus,



Observation will be output by state . This state can be any state from the set . A state in the set of states is indexed by or .,



When the state in the sequence of states is state , it is defined as follows



The above example can thus be redrawn as

## Transition Probability Matrix

Transitions between the states are expressed as probabilities. denotes the probability of moving from state to state .



A state has a transition probability to every other state in and the final state .

By the law of total probability



The matrix can be defined as follows. Each row must add up to .



For simplicity, here is an example configuration

The Transition Probability Matrix for the above configuration will be



## Observation Likelihood

The emission of an observation by a state is also expressed as a probability. is the probability of an observation being generated from a state . These are also called emission probabilities.



Every state in has an emission probability for observation .

The matrix can be defined as follows



Extending the previous example

The Observation Likelihood Matrix for the above configuration will be



## Hidden Markov Model

A Hidden Markov Model is thus defined by the above two matrices and . This is written as



In addition, a first-order Hidden Markov Model also follows two simplifying assumptions.

## Markov Assumption

The probability of a particular state depends only on the previous state



## Output Independence

The probability of an output observation , depends only on the state that produced the observation and not on any other states or any other observations



### Thank You

• Mark – for pointing out typos and errors

## 2 Comments →Visualizing Hidden Markov Models

1. Mark

Thank you for the excellent write up! Very clearly explained. However, I believe the example state transition probability matrix (A) should have the 0.3 and 0.5 values in swapped positions on the bottom row (i.e. 0.3 0.5 0.2)