Resumen de Structure Learning of High-Order Dynamic Bayesian Networks via Particle Swarm Optimization with Order Invariant Encoding
David Quesada López, Concha Bielza Lozoya 
, Pedro Larrañaga Múgica
Dynamic Bayesian networks usually make the assumption that the underlying process they model is first-order Markovian, that is, that the future state is independent of the past given the present. However, there are situations in which this assumption has to be relaxed. When this order increases, the size of the search space grows greatly, not all structure learning algorithms may be suited to learn higher-order networks, and a new appropriate order has to be found. To address the computational issues of huge networks, we propose a structure learning method that uses particle swarm optimization to search in the space of possible structures. To avoid the additional costs of increasing the Markovian order, we provide an order-invariant encoding that represents the networks as vectors of natural numbers whose length remains constant. Due to this encoding, we only need to set a maximum desired order rather than the exact one. Our experimental results show that this method is efficient in high orders and performs better than similar algorithms in both execution time and quality of the obtained networks.
