Deep Learning: Foundations and Concepts

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11. STRUCTURED DISTRIBUTIONS 11.18 (?) Consider a second-order Markov process described by the graph in Figure 11.30. By combining adjacent pairs of variables, show that this can be expressed as a firstorder Markov process over the new variables. 11.19 (?) By using d-separation, show that the distribution p(x1 , . . . , xN ) of the observed data for the state-space model represented by the directed graph in Figure 11.31 does not satisfy any conditional independence properties and hence does not exhibit the Markov property at any finite order.


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Deep Learning: Foundations and Concepts by Chris Bishop - Issuu