Mercurial > hg > camir-aes2014
comparison 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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-1:000000000000 | 0:e9a9cd732c1e |
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1 function engine = kalman_inf_engine(bnet) | |
2 % KALMAN_INF_ENGINE Inference engine for Linear-Gaussian state-space models. | |
3 % engine = kalman_inf_engine(bnet) | |
4 % | |
5 % 'onodes' specifies which nodes are observed; these must be leaves. | |
6 % The remaining nodes are all hidden. All nodes must have linear-Gaussian CPDs. | |
7 % The hidden nodes must be persistent, i.e., they must have children in | |
8 % the next time slice. In addition, they may not have any children within the current slice, | |
9 % except to the observed leaves. In other words, the topology must be isomorphic to a standard LDS. | |
10 % | |
11 % There are many derivations of the filtering and smoothing equations for Linear Dynamical | |
12 % Systems in the literature. I particularly like the following | |
13 % - "From HMMs to LDSs", T. Minka, MIT Tech Report, (no date), available from | |
14 % ftp://vismod.www.media.mit.edu/pub/tpminka/papers/minka-lds-tut.ps.gz | |
15 | |
16 [engine.trans_mat, engine.trans_cov, engine.obs_mat, engine.obs_cov, engine.init_state, engine.init_cov] = ... | |
17 dbn_to_lds(bnet); | |
18 | |
19 % This is where we will store the results between enter_evidence and marginal_nodes | |
20 engine.one_slice_marginal = []; | |
21 engine.two_slice_marginal = []; | |
22 | |
23 engine = class(engine, 'kalman_inf_engine', inf_engine(bnet)); |