function engine = kalman_inf_engine(bnet)% KALMAN_INF_ENGINE Inference engine for Linear-Gaussian state-space models.% engine = kalman_inf_engine(bnet)%% 'onodes' specifies which nodes are observed; these must be leaves.% The remaining nodes are all hidden. All nodes must have linear-Gaussian CPDs.% The hidden nodes must be persistent, i.e., they must have children in% the next time slice. In addition, they may not have any children within the current slice,% except to the observed leaves. In other words, the topology must be isomorphic to a standard LDS.%% There are many derivations of the filtering and smoothing equations for Linear Dynamical% Systems in the literature. I particularly like the following% - "From HMMs to LDSs", T. Minka, MIT Tech Report, (no date), available from%    ftp://vismod.www.media.mit.edu/pub/tpminka/papers/minka-lds-tut.ps.gz[engine.trans_mat, engine.trans_cov, engine.obs_mat, engine.obs_cov, engine.init_state, engine.init_cov] = ...    dbn_to_lds(bnet);% This is where we will store the results between enter_evidence and marginal_nodesengine.one_slice_marginal = [];engine.two_slice_marginal = [];engine = class(engine, 'kalman_inf_engine', inf_engine(bnet));