## What do you understand by forward and backward algorithms?

The algorithm makes use of the principle of dynamic programming to efficiently compute the values that are required to obtain the posterior marginal distributions in two passes. The first pass goes forward in time while the second goes backward in time; hence the name forward–backward algorithm.

**What is backward algorithm used for?**

Like the forward algorithm, we can use the backward algorithm to calculate the marginal likelihood of a hidden Markov model (HMM). Also like the forward algorithm, the backward algorithm is an instance of dynamic programming where the intermediate values are probabilities.

**How is Viterbi decoding different from forward algorithm?**

Forward-Backward gives marginal probability for each individual state, Viterbi gives probability of the most likely sequence of states.

### What is forward algorithm used for?

The forward algorithm is used to determine the probability of being in a state given a sequence of observations. For each observation you take the probabilities over the states computed for the previous observation, and then extend it out one more step using the transition probability table.

**What is Alpha in forward algorithm?**

Here αj(t) α j ( t ) is the probability that the machine will be at hidden state sj at time step t , after emitting first t visible sequence of symbols.

**What is forward approach?**

In the working-forward approach, as the name implies, the problem solver tries to solve the problem from beginning to end. A trip from New York City to Boston might be planned simply by consulting a map and establishing the shortest route that originates in New York City…

#### What is forward probability?

The forward algorithm, in the context of a hidden Markov model (HMM), is used to calculate a ‘belief state’: the probability of a state at a certain time, given the history of evidence. The process is also known as filtering. The forward algorithm is closely related to, but distinct from, the Viterbi algorithm.

**Why is Viterbi algorithm important?**

The Viterbi algorithm provides an efficient way of finding the most likely state sequence in the maximum a posteriori probability sense of a process assumed to be a finite-state discrete-time Markov process. Such processes can be subsumed under the general statistical framework of compound decision theory.

**What is Viterbi receiver?**

A Viterbi decoder uses the Viterbi algorithm for decoding a bitstream that has been encoded using a convolutional code or trellis code. It is most often used for decoding convolutional codes with constraint lengths k≤3, but values up to k=15 are used in practice.

## What is hidden state in HMM?

Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process — call it — with unobservable (“hidden”) states. As part of the definition, HMM requires that there be an observable process whose outcomes are “influenced” by the outcomes of in a known way.