Читать книгу Nonlinear Filters - Simon Haykin - Страница 12

Backprop KFbackpropagation Kalman filterBMSbattery management systemsCKFcubature Kalman filterCNNconvolutional neural networkCRLBCramér–Rao lower boundDDFdivided‐difference filterDKFdeep Kalman filterDRBPFdeep Rao–Blackwellized particle filterDVBFdeep variational Bayes filterDVIBdeep variational information bottleneckEKFextended Kalman filterELBOevidence lower boundEMexpectation maximizationFATEfairness, accountability, transparency, and ethicsGANgenerative adversarial networkGRUgated recurrent unitHINThierarchical invertible neural transportIBinformation bottleneckIMMinteracting multiple modelISimportance samplingKFKalman filterKLDKullback–Leibler divergenceKVAEKalman variational autoencoderLSTMlong short‐term memoryLTIlinear time‐invariantLTVlinear time‐varyingMAPmaximum a posterioriMCMCMarkov chain Monte CarloMDPMarkov decision processMLmaximum likelihoodMMSEminimum mean square errorMPFmarginalized particle filterN‐EMneural expectation maximizationNIBnonlinear information bottleneckNLLnegative log likelihoodPCRLBposterior Cramér–Rao lower boundPDFprobability distribution functionP‐EMparticle expectation maximizationPFparticle filterPIDproportional‐integral‐derivativePOMDPpartially‐observable Markov decision processRBPFRao‐Blackwellised particle filterReLUrectified linear unitR‐NEMrelational neural expectation maximizationRNNrecurrent neural networkSGVBstochastic gradient variational BayesSIRsampling importance resamplingSLAMsimultaneous localization and mappingSMAUGsingle molecule analysis by unsupervised Gibbs samplingSMCsequential Monte CarloSoCstate of chargeSoHstate of healthSVSFsmooth variable‐structure filterTD learningtemporal‐difference learningUIOunknown‐input observerUKFunscented Kalman filterVAEvariational autoencoderVFEMvariational filtering expectation maximizationVSCvariable‐structure controlwILIweighted influenza‐like illness