comparison toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/kalman_inf_engine.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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));