Hidden Markov Models (HMM)
Hidden Markov Models (HMM)#
Hidden Markov Models (HMMs) are a type of probabilistic graphical model that are used for modeling sequential data. They are commonly used in fields such as speech recognition, natural language processing, and bioinformatics.
How Do HMMs Work?#
An HMM consists of a hidden state sequence and an observable sequence. The hidden state sequence represents the underlying process that generates the observations, while the observable sequence is the sequence of observations that are directly visible. In essence, the goal of HMM is to model the probability distribution of the hidden state sequence given the observed sequence.
One of the key advantages of HMMs is that they allow for modeling the time-dependence of the data. This means that the probabilities of the hidden states are influenced by the previous hidden states. This makes HMMs well-suited for modeling sequences of data that have a temporal structure, such as speech signals or sequences of words in a sentence.
Another advantage of HMMs is that they are flexible in the sense that they can handle missing or partially observable data. This is because the hidden states are not directly observable, but instead are inferred from the observed sequence.
There are some disadvantages of HMMs, such as their assumption of Markovian dynamics and their sensitivity to the choice of initialization parameters. However, despite these limitations, HMMs have proven to be powerful models for a wide range of sequential data problems.
HMMs can be trained using the Baum-Welch algorithm, which is an iterative method that estimates the parameters of the HMM given a training set of observed sequences. These parameters can then be used to make predictions about future observations given a sequence of previous observations.
The Baum-Welch algorithm is based on the principle of expectation-maximization (EM), a popular technique for estimating parameters in probabilistic models.
The Baum-Welch algorithm consists of two main steps: the expectation (E) step and the maximization (M) step. In the E step, the algorithm computes the expected counts of each hidden state and transition given the observed data and the current estimates of the model parameters. In the M step, the algorithm uses these expected counts to compute new estimates of the model parameters that maximize the likelihood of the observed data given the current estimates of the model.
The Baum-Welch algorithm is an iterative process, where the E and M steps are repeated until the model parameters converge to a local maximum of the likelihood. The algorithm starts with an initial estimate of the model parameters and iteratively refines these estimates until convergence, resulting in an HMM that best explains the observed data.
The forward-backward algorithm is a widely used algorithm for solving the inference problem in Hidden Markov Models (HMMs). It provides an efficient way to calculate the likelihood of a sequence of observations given the model parameters and to estimate the hidden states that generated these observations.
The forward-backward algorithm consists of two steps: the forward step and the backward step.
The forward step starts from the first observation and calculates the forward probability, which is the probability of observing the first observation and being in a specific state at time t=1. It then uses this forward probability to calculate the forward probability of observing the second observation and being in a specific state at time t=2, and so on until the end of the sequence. The forward step can be seen as calculating the probability of observing the entire sequence given the model parameters and starting from each possible state.
The backward step starts from the last observation and calculates the backward probability, which is the probability of observing the rest of the sequence given that we are in a specific state at the end. It then uses the backward probability to calculate the probability of observing the rest of the sequence given that we are in a specific state at time t-1, and so on until the beginning of the sequence. The backward step can be seen as calculating the probability of observing the rest of the sequence given the model parameters and ending in each possible state.
Finally, the forward-backward algorithm combines the forward and backward probabilities to estimate the most likely hidden states that generated the sequence of observations. The probabilities of being in each state at each time step can be calculated using the forward and backward probabilities and the transition probabilities between states.
The forward-backward algorithm is widely used in speech recognition, gene expression analysis, and other applications that involve modeling sequences of observations with hidden states. It provides a flexible and efficient solution for solving the inference problem in HMMs.
The Viterbi algorithm is a dynamic programming algorithm used to solve the maximum likelihood decoding problem for hidden Markov models (HMMs). The algorithm is named after its creator, Andrew Viterbi, who developed it in 1967.
The purpose of the Viterbi algorithm is to find the most likely sequence of hidden states that generated a given observation sequence (called the “decoding task”). Given an observation sequence and an HMM, the algorithm iteratively updates the probability of being in each state at each time step based on the observation and the transition probabilities between states. The final output is the sequence of hidden states with the highest overall probability.
The Viterbi algorithm starts by computing the initial probabilities of being in each state given the first observation. It then iteratively updates the probabilities of being in each state at each time step by considering all possible previous states and choosing the state with the highest probability. This process continues until the final time step is reached and the algorithm outputs the final sequence of states with the highest overall probability.
The Viterbi algorithm has many applications in natural language processing, speech recognition, and bioinformatics, among others. It is fast and efficient and has become a standard tool for solving maximum likelihood decoding problems for HMMs.
Example code for decoding:
import numpy as np from hmmlearn import hmm # Define the observations observations = np.array([[1, 0, 1, 0, 1], [0, 1, 0, 1, 0], [0, 0, 0, 1, 1]]).reshape(-1, 1) # Define the lengths of the sequences lengths = [5, 5, 5] # Define the CategoricalHMM model model = hmm.CategoricalHMM(n_components=2) # Fit the model to the observations model = model.fit(observations, lengths) # Predict the hidden states hidden_states = model.predict(observations) # Get the probability of the observations prob = model.score(observations, lengths) # Print the hidden states and the probability print("Hidden States: ", hidden_states) print("Probability of Observations: ", prob)
In conclusion, Hidden Markov Models (HMMs) are a popular tool for modeling sequential data. They have been successfully applied to a wide range of problems, such as speech recognition, biological sequence analysis, and finance. The key idea behind HMMs is to model the underlying state sequence of a process, while observing only the observable outputs. The Baum-Welch algorithm and the Viterbi algorithm are two essential algorithms for working with HMMs. The Baum-Welch algorithm uses the expectation-maximization technique to estimate the model parameters, and the Viterbi algorithm is used to find the most likely state sequence given the observed data. In this chapter, we have seen how these algorithms work and how they can be applied to real-world problems. Understanding HMMs and these algorithms can be a valuable addition to any machine learning engineer’s toolkit.
Where to Learn More#
I’ve covered HMMs in-depth in the following course:
Unsupervised Machine Learning: Hidden Markov Models in Python