Hey folks

For the next session we will be focusing on Discrete HMMs. The next session will be live session will be on **Sunday the 25th**

Our goal is to fully understand this formula

\begin{align}
p(y_{1:T}, z_{1:T} \mid \theta)
&= \mathrm{Cat}(z_1 \mid \pi)
\prod_{t=2}^T \mathrm{Cat}(z_t \mid A_{z_{t-1}})
\prod_{t=1}^T \mathrm{Cat}(y_t \mid B_{z_t})
\end{align}

We’ll do that with the following reading material

- Casino HMMs from Dynamax
- Complete reading of Exploring Hidden Markov Models

Focus on

- Filtering (forwards algorithm)
- Smoothing (forwards-backwards algorithm)
- Most likely state sequence (Viterbi algorithm)

As always this is just my prior. If you have opinions or suggestions please do leave them below!