代码搜索：Multinomial

找到约 224 项符合「Multinomial」的源代码

代码结果 224
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m entropic_map_estimate.m

function [theta, loglik] = entropic_map_estimate(counts, Z) % ENTROPIC_MAP_ESTIMATE Find MAP estimate of multinomial using entropic prior % [theta, loglik] = entropic_map_estimate(counts, Z) % % The e
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m my_entropic_map.m

function [theta, loglik] = my_entropic_map(counts, Z) % ENTROPIC_MAP_ESTIMATE Find MAP estimate of multinomial using entropic prior % [theta, loglik] = entropic_map_estimate(counts, Z) % % The entropi
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java multinomialdistributiontest.java

package com.aliasi.test.unit.stats; import com.aliasi.stats.MultinomialDistribution; import com.aliasi.stats.MultivariateConstant; import com.aliasi.test.unit.BaseTestCase; public class Multinomial
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c crc8.c

#include #define uchar unsigned char #define CMC 0x8c //crc8_multinomial_code #define LENGTH 24 //LENGTH = 24 extern uchar CMDIN[LEN
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m compute_suff_stats.m

function freq=compute_suff_stats(post,varargin) %compute suff stats for multinomial logist reg (freq of successes and total %counts) if isempty(varargin) merged_node=0; else merged_node=1; da
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m tabular_cpd.m

function CPD = tabular_CPD(bnet, self, varargin) % TABULAR_CPD Make a multinomial conditional prob. distrib. (CPT) % % CPD = tabular_CPD(bnet, node) creates a random CPT. % % The following argume
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m tabular_cpd.m

function CPD = tabular_CPD(bnet, self, varargin) % TABULAR_CPD Make a multinomial conditional prob. distrib. (CPT) % % CPD = tabular_CPD(bnet, node) creates a random CPT. % % The following argume
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m tabular_cpd.m

function CPD = tabular_CPD(bnet, self, varargin) % TABULAR_CPD Make a multinomial conditional prob. distrib. (CPT) % % CPD = tabular_CPD(bnet, node) creates a random CPT. % % The following argume
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www.eeworm.com/read/408292/11398091

m score.m

function score=score(link,parms,restparms,joint_prob_tabs,design,equiv_class,equiv_class_time,terminal_merged_nodes,N) %computes score function for multinomial regression in point parms nrparse
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m multinom_logit.m

function y=multinom_logit(x,baselinecategory) %every column of x is prob vector of size k, y is multinomial logit of length k-1 %if only one prob vector, use row vector! if nargin==1 baselin
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