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The probabilistic model

Let us denote the observed data with $ \boldsymbol{X}= (\mathbf{x}(1), \ldots, \mathbf{x}(T))$, the hidden state values with $ \boldsymbol{S}= ( \mathbf{s}(1), \ldots, \mathbf{s}(T) )$ and all the other model parameters with $ \boldsymbol {\theta }$. These other parameters consist of the weights and biases of the MLP networks and hyperparameters defining the prior distributions of other parameters.


Antti Honkela 2001-05-30