代码搜索：Probabilities
找到约 751 项符合「Probabilities」的源代码
代码结果 751
www.eeworm.com/read/336521/12439719
m huffman.m
function [cc,ll,l]=huffman(p,a)
%HUFFMAN calculates a D-ary huffman code [CC,LL]=(P,A)
%
% Inputs: P is a vector or matrix of probabilities
% A(D) is a vector of alphabet ch
www.eeworm.com/read/228372/14388086
m huffman.m
function [cc,ll,l]=huffman(p,a)
%HUFFMAN calculates a D-ary huffman code [CC,LL]=(P,A)
%
% Inputs: P is a vector or matrix of probabilities
% A(D) is a vector of alphabet ch
www.eeworm.com/read/122800/14667948
c dice.c
/*
Please see attachment for the sample program : It takes distribution
from stdin, and output to stdout(some information to stderr).
Probabilities don't need to sum up to 1. In the output, each ar
www.eeworm.com/read/221666/14730363
cc generate_seq.cc
// file : generate_seq.cc
// author: Richard Myers
// version: 1.03 [August 21, 1995]
//
// This program generates random sequences using the probabilities defined
// in a markov model file.
#incl
www.eeworm.com/read/119681/14824432
m bay_optimize.m
function [model,A,B,C,D] = bay_optimize(model,level, type, nb, bay)
% Optimize the posterior probabilities of model (hyper-) parameters with respect to the different levels in Bayesian inference
%
%
www.eeworm.com/read/214923/15082902
m bay_optimize.m
function [model,A,B,C,D] = bay_optimize(model,level, type, nb, bay)
% Optimize the posterior probabilities of model (hyper-) parameters with respect to the different levels in Bayesian inference
%
%
www.eeworm.com/read/213529/15130694
cc generate_seq.cc
// file : generate_seq.cc
// author: Richard Myers
// version: 1.03 [August 21, 1995]
//
// This program generates random sequences using the probabilities defined
// in a markov model file.
#incl
www.eeworm.com/read/251838/4414631
m mk_rnd_map_hhmm.m
function bnet = mk_rnd_map_hhmm(varargin)
% We copy the deterministic structure of the real HHMM,
% but randomize the probabilities of the adjustable CPDs.
% The key trick is that 0s in the real
www.eeworm.com/read/251522/4418843
m cg1.m
function cg1
% The waste incinerator emissions example from Lauritzen (1992),
% "Propogation of Probabilities, Means and Variances in Mixed Graphical Association Models",
% JASA 87(420): 1098--1108
www.eeworm.com/read/251522/4418913
m mk_rnd_map_hhmm.m
function bnet = mk_rnd_map_hhmm(varargin)
% We copy the deterministic structure of the real HHMM,
% but randomize the probabilities of the adjustable CPDs.
% The key trick is that 0s in the real