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/183445/9158756
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
www.eeworm.com/read/374698/9388936
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
www.eeworm.com/read/278889/10490713
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
www.eeworm.com/read/421949/10676249
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
www.eeworm.com/read/449504/7502170
m ols_g.m
function results = ols_g(y,x,ndraw,nomit,prior,start)
% PURPOSE: MCMC estimates for the Bayesian heteroscedastic linear model
% y = X B + E, E = N(0,sige*V),
% V = diag(v1,v2,...
www.eeworm.com/read/449504/7502913
m far_g.m
function results = far_g(y,W,ndraw,nomit,prior)
% PURPOSE: Bayesian estimates for the 1st-order Spatial autoregressive model
% y = rho*W*y + e, e = N(0,sige*V),
% V = diag(v1,
www.eeworm.com/read/449504/7503074
m sar_c.m
function results = sar_c(y,x,W,prior)
% PURPOSE: Bayesian log-marginal posterior for the spatial autoregressive model
% y = rho*W*y + XB + e, e = N(0,sige*In)
% B = N[0,inv(g*X'X)
www.eeworm.com/read/440842/7680299
m ols_g.m
function results = ols_g(y,x,ndraw,nomit,prior,start)
% PURPOSE: MCMC estimates for the Bayesian heteroscedastic linear model
% y = X B + E, E = N(0,sige*V),
% V = diag(v1,v2,...
www.eeworm.com/read/397122/8065901
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