One use of the pseudolikelihood measure is as an approximation for inference about a Markov or Bayesian network, as the pseudolikelihood of an assignment to may often be computed more efficiently than the likelihood, particularly when the latter may require marginalization over a large number of variables.
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The inherent randomness of algorithms such as Hyper-encryption, Bayesian networks, Random neural networks and Probabilistic Cellular Automata was harnessed by Krishna Palem et al. to design highly efficient hardware systems using Probabilistic CMOS or PCMOS technology that were shown to achieve gains that are as high as a multiplicative factor of 560 when compared to a competing energy-efficient CMOS based realizations.