代码搜索：Inference
找到约 1,820 项符合「Inference」的源代码
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www.eeworm.com/read/251522/4419049
m belprop_fg_inf_engine.m
function engine = belprop_fg_inf_engine(fg, varargin)
% BELPROP_FG_INF_ENGINE Make a belief propagation inference engine for factor graphs
% engine = belprop_fg_inf_engine(factor_graph, ...)
%
% The
www.eeworm.com/read/251522/4419147
m hmm_inf_engine.m
function engine = hmm_inf_engine(bnet, varargin)
% HMM_INF_ENGINE Inference engine for DBNs which uses the forwards-backwards algorithm.
% engine = hmm_inf_engine(bnet, ...)
%
% The following optional
www.eeworm.com/read/251522/4419166
m jtree_dbn_inf_engine.m
function engine = jtree_dbn_inf_engine(bnet, varargin)
% JTREE_DBN_INF_ENGINE Junction tree inference algorithm for DBNs.
% engine = jtree_inf_engine(bnet, ...)
%
% The following optional arguments ca
www.eeworm.com/read/225759/4792617
m cond_gauss_inf_engine.m
function engine = cond_gauss_inf_engine(bnet)
% COND_GAUSS_INF_ENGINE Conditional Gaussian inference engine
% engine = cond_gauss_inf_engine(bnet)
%
% Enumerates all the discrete roots, and runs jtree
www.eeworm.com/read/225759/4792666
m var_elim_inf_engine.m
function engine = var_elim_inf_engine(bnet)
% VAR_ELIM_INF_ENGINE Variable elimination inference engine
% engine = var_elim_inf_engine(bnet)
%
% The variable elimination algorithm (also known as bucke
www.eeworm.com/read/225759/4792694
m ff_inf_engine.m
function engine = ff_inf_engine(bnet, onodes)
% FF_INF_ENGINE Factored frontier inference engine for DBNs
% engine = ff_inf_engine(bnet, onodes)
%
% The model must obey the same topological restrictio
www.eeworm.com/read/223787/4808877
java tuple.java
//: net/mindview/util/Tuple.java
// Tuple library using type argument inference.
package net.mindview.util;
public class Tuple {
public static TwoTuple tuple(A a, B b) {
return
www.eeworm.com/read/215485/4903387
m scg3.m
% Compare various inference engines on the following network (from Jensen (1996) p84 fig 4.17)
% 1
% / | \
% 2 3 4
% | | |
% 5 6 7
% \/ \/
% 8 9
% where all arcs point downwards
N = 9;
www.eeworm.com/read/215485/4903491
m discrete1.m
% Compare various inference engines on the following network (from Jensen (1996) p84 fig 4.17)
% 1
% / | \
% 2 3 4
% | | |
% 5 6 7
% \/ \/
% 8 9
% where all arcs point downwards
N = 9;
www.eeworm.com/read/215485/4903529
m chmm1.m
% Compare the speeds of various inference engines on a coupled HMM
N = 2;
Q = 2;
rand('state', 0);
randn('state', 0);
discrete = 1;
if discrete
Y = 2; % size of output alphabet
else
Y = 1;
end
co