代码搜索：Inference

找到约 1,820 项符合「Inference」的源代码

代码结果 1,820
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m hmm_2tbn_inf_engine.m

function engine = hmm_2TBN_inf_engine(bnet, varargin) % HMM_2TBN_INF_ENGINE Inference engine for DBNs which uses the forwards-backwards algorithm. % engine = hmm_2TBN_inf_engine(bnet, ...) % % The DBN
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packages

org.apache.commons.math.stat org.apache.commons.math.stat.regression org.apache.commons.math.stat.inference org.apache.commons.math.stat.descriptive.summary org.apache.commons.math.stat.descriptive or
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packages

org.apache.commons.math.stat org.apache.commons.math.stat.regression org.apache.commons.math.stat.inference org.apache.commons.math.stat.descriptive.summary org.apache.commons.math.stat.descriptive or
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www.eeworm.com/read/160391/5571213

m scg_dbn.m

% Test whether stable conditional Gaussian inference works % Make a linear dynamical system % X1 -> X2 % | | % v v % Y1 Y2 intra = zeros(2); intra(1,2) = 1; inter = zero
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prj proj.prj

#-- Synplicity, Inc. #-- Version 7.0.3 #-- Project file G:\XROADS\INFERENCE_STATUS\SPRO_703\VERILOG\BUFGCE_INSTANCIATE\proj.prj #-- Written on Thu Feb 14 15:58:24 2002 #add_file options ad
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prj proj.prj

#-- Synplicity, Inc. #-- Version 7.0.3 #-- Project file G:\XROADS\INFERENCE_STATUS\SPRO_703\VERILOG\DDR_INSTANCIATE\OUTPUT_INSTANCIATE\proj.prj #-- Written on Thu Feb 14 15:54:37 2002 #add_
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vhd 各种功能的计数器.vhd

-- MAX+plus II VHDL Example -- Efficient Counter Inference -- Copyright (c) 1994 Altera Corporation -- download from:www.pld.com.cn & www.fpga.com.cn Library IEEE ; use IEEE.std_logic_1164.all
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vhd 带load、clr等功能的寄存器.vhd

-- Register Inference -- Download from: http://www.fpga.com.cn Library IEEE ; use IEEE.std_logic_1164.all ; ENTITY reginf IS PORT ( d, clk, clr, pre, load, data : IN BIT; q1, q2,
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www.eeworm.com/read/172172/9722143

m bay_lssvm.m

function [A,B,C,D,E] = bay_lssvm(model,level,type, nb, bay) % Compute the posterior cost for the 3 levels in Bayesian inference % % >> cost = bay_lssvm({X,Y,type,gam,sig2}, level, type) % >> cost = b
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www.eeworm.com/read/367440/9748524

m bay_lssvm.m

function [A,B,C,D,E] = bay_lssvm(model,level,type, nb, bay) % Compute the posterior cost for the 3 levels in Bayesian inference % % >> cost = bay_lssvm({X,Y,type,gam,sig2}, level, type) % >> cost = b
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