代码搜索：Inference

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m jtree_2tbn_inf_engine.m

function engine = jtree_2TBN_inf_engine(bnet, varargin) % JTREE_ONLINE_INF_ENGINE Online Junction tree inference algorithm for DBNs. % engine = jtree_online_inf_engine(bnet, ...) % % The following opt
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m jtree_2tbn_inf_engine.m

function engine = jtree_2TBN_inf_engine(bnet, varargin) % JTREE_ONLINE_INF_ENGINE Online Junction tree inference algorithm for DBNs. % engine = jtree_online_inf_engine(bnet, ...) % % The following opt
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www.eeworm.com/read/393163/2488350

m jtree_sparse_2tbn_inf_engine.m

function engine = jtree_sparse_2TBN_inf_engine(bnet, varargin) % JTREE_ONLINE_INF_ENGINE Online Junction tree inference algorithm for DBNs. % engine = jtree_online_inf_engine(bnet, ...) % % The fo
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www.eeworm.com/read/393163/2488453

m belprop_gdl_inf_engine.m

function engine = belprop_gdl_inf_engine(gdl, varargin) % BELPROP_GDL_INF_ENGINE Make a belief propagation inference engine for a GDL graph % engine = belprop_gdl_inf_engine(gdl_graph, ...) % % If th
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m dhmm_inf_engine.m

function engine = dhmm_inf_engine(bnet, onodes) % DHMM_INF_ENGINE Inference engine for discrete DBNs which uses the forwards-backwards algorithm. % engine = dhmm_inf_engine(bnet, onodes) % % 'onodes'
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m jtree_dbn_inf_engine.m

function engine = jtree_dbn_inf_engine(bnet, varargin) % JTREE_DBN_INF_ENGINE Junction tree inference algorithm for DBNs. ss = length(bnet.intra); onodes = []; if nargin >= 2 args = varargin; n
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m jtree_dbn_inf_engine1.m

function engine = jtree_dbn_inf_engine(bnet, varargin) % JTREE_DBN_INF_ENGINE Junction tree inference algorithm for DBNs. ss = length(bnet.intra); onodes = []; if nargin >= 2 args = varargin; n
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www.eeworm.com/read/393163/2488669

m jtree_dbn_inf_engine2.m

function engine = jtree_dbn_inf_engine(bnet, varargin) % JTREE_DBN_INF_ENGINE Junction tree inference algorithm for DBNs. ss = length(bnet.intra); onodes = []; if nargin >= 2 args = varargin; n
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www.eeworm.com/read/160391/5571223

m online1.m

% Check that online inference gives same results as filtering for various algorithms N = 3; Q = 2; ss = N*2; rand('state', 0); randn('state', 0); obs_size = 1; discrete_obs = 0; bnet =
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www.eeworm.com/read/160391/5571234

m cmp_learning_dbn.m

function [time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, T, varargin) % CMP_LEARNING_DBN Compare a bunch of inference engines by learning a DBN % function [time, CPD, LL, cases] = cmp_learni
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