Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/kalman_inf_engine.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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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_nodes engine.one_slice_marginal = []; engine.two_slice_marginal = []; engine = class(engine, 'kalman_inf_engine', inf_engine(bnet));