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27 Hidden Markov Models

An elementary first-order Hidden Markov model is a probabilistic model over $N$ observations, $y_n$, and $N$ hidden states, $x_n$, which can be fully defined by the conditional distributions $p(y_n \mid x_n, \phi)$ and $p(x_n \mid x_{n - 1}, \phi)$. Here we make the dependency on additional model parameters, $\phi$, explicit. When $x$ is continuous, the user can explicitly encode these distributions in Stan and use Markov chain Monte Carlo to integrate $x$ out.

When each state $x$ takes a value over a discrete and finite set, say $\{1, 2, ..., K\}$, we can take advantage of the dependency structure to marginalize $x$ and compute $p(y \mid \phi)$. We start by defining the conditional observational distribution, stored in a $K \times N$ matrix $\omega$ with $$\omega_{kn} = p(y_n \mid x_n = k, \phi).$$ Next, we introduce the $K \times K$ transition matrix, $\Gamma$, with $$\Gamma_{ij} = p(x_n = j \mid x_{n - 1} = i, \phi).$$ Each row defines a probability distribution and must therefore be a simplex (i.e. its components must add to 1). Currently, Stan only supports stationary transitions where a single transition matrix is used for all transitions. Finally we define the initial state $K$-vector $\rho$, with $$\rho_k = p(x_0 = k \mid \phi).$$

The Stan functions that support this type of model are special in that the user does not explicitly pass $y$ and $\phi$ as arguments. Instead, the user passes $\log \omega$, $\Gamma$, and $\rho$, which in turn depend on $y$ and $\phi$.