代码搜索：binomial
找到约 467 项符合「binomial」的源代码
代码结果 467
www.eeworm.com/read/107175/15612161
h confidence_intervals.h
/* confidence_intervals.h */
/* functions provided by confidence_intervals.c */
double binomial_p(double N, double p, double x1, double x2);
double binomial_low(double vx, double vN, double p);
doubl
www.eeworm.com/read/452284/7442655
m bincof2.m
function w = bincof2(N,L)
% BINCOF2
% MATLAB m-file for calculating binomial coefficients
% Format: w = bincof2(N,L)
% w = ( N ) = N!/(L!(N-L)!)
% ( L )
% Subroutines:standard MATLAB functio
www.eeworm.com/read/449504/7501991
m bino_cdf.m
function cdf = bino_cdf (x, n, p)
% PURPOSE: cdf at x of the binomial(n,p) distribution
%---------------------------------------------------
% USAGE: cdf = bino_cdf(x,n,p)
% where: p = the probabi
www.eeworm.com/read/483033/6607873
m bin_pdf.m
%BIN_PDF Probability density function of a Binomial distribution.
%
% Syntax:
% p = bin_pdf(x,rho,n)
%
% In:
% x - Location where to evaluate the PDF.
% rho - 'Probability' parameter
% n
www.eeworm.com/read/343227/11962653
m ex_cnt.m
%ex_cnt Script to illustrate the three basic models handled by H2m/cnt
% - Poisson mixtures
% - Poisson hidden Markov models
% - Negative-binomial hidden Markov mod
www.eeworm.com/read/343227/11962656
m nbh_em.m
function [TRANS, alpha, beta, logl, bckwrd,dens] = nbh_em(count, TRANS, alpha, beta, Nit)
%nbh_em Estimates the parameters of a negative binomial HMM using EM.
% Use: [TRANS,alpha,beta,logl
www.eeworm.com/read/104929/15680999
ref chap5.ref
|bc-def {1}
|pascal-triangle {155}
|bc-factorial {3}
|bc-symmetry {4}
|bc-absorb {5}
|bc-absorb-k {6}
|bc-absorb-r-k {7}
|bc-addition {8}
|bc-sum-both {9}
|bc-sum-upper {10}
|bin-thm-xy {12}
|bin-thm-
www.eeworm.com/read/433114/8545668
m binom.m
function bin = binom(a,n,signum)
% Function computes n binomial coefficients
% 1 sign*a (sign^2)*a*(a-1)/2 (sign^3)*a*(a-1)*(a-2)/3! ...
% If a is a positive integer n can be dropped (it is th
www.eeworm.com/read/307313/13724304
txt tryrand.txt
Begin test
Now print a real number: 3.14159
Print 20 N(0,1) random numbers - should be the same as in sample output
1.60382
-1.30754
-1.35107
-1.13924
0.273018
1.06615
-0.124875
-0.948578
0.275591
1.