Which of the following scenarios is most appropriate for applying a Hidden Markov Model?
Question 2
In the context of the Forward-Backward Algorithm, what does the 'backward variable' $\beta_t(i)$ represent?
Question 3
Given an HMM with initial state probabilities $\pi = [0.8, 0.2]$ for states $S_1$ and $S_2$ respectively. If the first observation is $O_A$, and the emission probability of $O_A$ from $S_1$ is $P(O_A | S_1) = 0.6$, what is the probability of being in state $S_1$ at time $t=1$ and observing $O_A$?
Question 4
Which of the following is a key challenge that the Expectation-Maximization (EM) algorithm helps address in Hidden Markov Models?
Question 5
A fundamental assumption of Hidden Markov Models is that the current hidden state depends only on the immediately preceding hidden state. What is this property known as?
