代码搜索：Bayesian
找到约 1,632 项符合「Bayesian」的源代码
代码结果 1,632
www.eeworm.com/read/428780/1954155
asv exp.asv
%Example: 贝叶斯分类器
% BAYESCLS Bayesian classifier with reject option.
%
% Synopsis:
% [y, dfce] = bayescls(X,model)
%
% Description:
% This function implements the classifier minimizing the B
www.eeworm.com/read/428780/1954196
asv exp.asv
%Example: 贝叶斯分类器
% BAYESCLS Bayesian classifier with reject option.
%
% Synopsis:
% [y, dfce] = bayescls(X,model)
%
% Description:
% This function implements the classifier minimizing the B
www.eeworm.com/read/411674/11232971
m bayescls.m
function [y, dfce] = bayescls( X, model )
% BAYESCLS Bayesian classifier with reject option.
%
% Synopsis:
% [y, dfce] = bayescls(X,model)
%
% Description:
% This function implements the classifier
www.eeworm.com/read/200584/15429361
m bayescls.m
function [y, dfce] = bayescls( X, model )
% BAYESCLS Bayesian classifier with reject option.
%
% Synopsis:
% [y, dfce] = bayescls(X,model)
%
% Description:
% This function implements the classifier
www.eeworm.com/read/284258/8952364
rd sbgcop.package.rd
\name{sbgcop-package}
\alias{sbgcop-package}
\alias{sbgcop}
\docType{package}
\title{
Semiparametric Bayesian Gaussian copula estimation
}
\description{
This package estimates parameters of a Gaussia
www.eeworm.com/read/182374/9205839
m learnbn.m
function[bnet] = LearnBN(NumbVar,Card,SelPop,TypeLearning,MaxParent,epsilon,mwst,star,SCORE,cachesize)
% Learns the Bayesian network from the selected population
% INPUTS
% NumbVar: Number of varia
www.eeworm.com/read/375967/9341178
pf
% an example of Generic Particle Filter
% Acorrding to the article "A Tutorial on Particle Filters for Online
% Nonlinear/Non-Gaussian Bayesian Tracking"by M.Sanjeev Arulampalam, Simon Maskell,
% N
www.eeworm.com/read/373623/9446339
r bart.r
### Name: bart
### Title: Bayesian Additive Regression Trees
### Aliases: bart plot.bart
### Keywords: nonparametric tree regression nonlinear
### ** Examples
##simulate data (example from Fr
www.eeworm.com/read/449504/7502076
m rvar.m
function results = rvar(y,nlag,w,freq,sig,tau,theta,x);
% PURPOSE: Estimates a Bayesian vector autoregressive model
% using the random-walk averaging prior
%--------------------------------
www.eeworm.com/read/196932/8040048
m bcmgrad.m
function g = bcmgrad(net, x, t)
% bcmgrad - Error gradient for Bayesian Committee Machine
%
% Synopsis:
% g = bcmgrad(net)
%
% Arguments:
% net: BCM structure
%
% Returns:
% g: Gradient of