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Inspired from the algorithm introduced by [40], the proposed BA algorithm is designed to work with discrete variables.
Note that we work with discrete and not continuous data and hence, also use the exact methods necessary to cope with discrete data.
We show that this leads to strong beyond-pairwise correlations that is, correlations that cannot be captured by maximum entropy models that extrapolate from pairwise statistics—as for earlier work with discrete threshold crossing (dichotomous Gaussian) models.
Fig. 5 shows the latency reduction (with respect to the beginning of the pattern) during the learning stage until it stabilizes at a minimum of about 4 ms. Apart from the initial part (before selectivity emerges) the curve looks similar to those observed in earlier work with discrete spike volleys[17].
It can be seen that STDP has potentiated most of the synapses that correspond to the earliest spikes of the pattern (Fig. 6a), and depressed most of the synapses that correspond to presynaptic spikes which follow the postsynaptic one, as in the previous work with discrete volleys [15], [17], [18].
This allows to work with discrete integral operators, i.e. matrices, in the implementation and leads to some perturbed Galerkin formulation given in Section 2.3.
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The Bayesian network works with discrete intervals for nodes.
J48 algorithm is a modified version of C4.5 and ID3 algorithm implemented through Waikato Environment for Knowledge Analysis (WEKA) software works with discrete and continuous variables and generates the trees (University of Waikato 2015).
The method works with discrete particles that transfer forces between each other through contact and elastic collisions, allowing for a simple and robust treatment of the interaction between the sand particles and the bullet which is represented by finite elements.
The RaTrav tool works with discrete time.
We work with a discrete-time two-period economy: t=0 and t=1.
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