代码搜索：Bayesian

找到约 1,632 项符合「Bayesian」的源代码

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m bay_modoutclass.m

function [Pplus, Pmin, bay,model] = bay_modoutClass(model,X,priorpos,type,nb,bay) % Estimate the posterior class probabilities of a binary classifier using Bayesian inference % % >> [Ppos, Pneg] = bay
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www.eeworm.com/read/316944/13514000

m bay_modoutclass.m

function [Pplus, Pmin, bay,model] = bay_modoutClass(model,X,priorpos,type,nb,bay) % Estimate the posterior class probabilities of a binary classifier using Bayesian inference % % >> [Ppos, Pneg] = bay
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www.eeworm.com/read/302157/13840872

cpp kalmanslam.cpp

/* * Bayes++ the Bayesian Filtering Library * Copyright (c) 2004 Michael Stevens * See accompanying Bayes++.htm for terms and conditions of use. * * $Header: /cvsroot/bayesclasses/Bayes++/SLAM/ka
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www.eeworm.com/read/302157/13840873

cpp fastslam.cpp

/* * Bayes++ the Bayesian Filtering Library * Copyright (c) 2004 Michael Stevens * See accompanying Bayes++.htm for terms and conditions of use. * * $Header: /cvsroot/bayesclasses/Bayes++/SLAM/fa
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www.eeworm.com/read/302157/13840874

cpp testfastslam.cpp

/* * Bayes++ the Bayesian Filtering Library * Copyright (c) 2004 Michael Stevens * See accompanying Bayes++.htm for terms and conditions of use. * * $Header: /cvsroot/bayesclasses/Bayes++/SLAM/te
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www.eeworm.com/read/302157/13840877

hpp slam.hpp

77/* * Bayes++ the Bayesian Filtering Library * Copyright (c) 2004 Michael Stevens * See accompanying Bayes++.htm for terms and conditions of use. * * $Header: /cvsroot/bayesclasses/Bayes++/SLAM/
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www.eeworm.com/read/302157/13840915

cpp covflt.cpp

/* * Bayes++ the Bayesian Filtering Library * See Bayes++.htm for copyright license details * Copyright (c) 2002 Michael Stevens * See accompanying Bayes++.htm for terms and conditions of use. *
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www.eeworm.com/read/302157/13840937

v2 jamfile.v2

# Bayes++ Jamfile - See Boost.build v2 # BayesFilter - The Bayesian filtering library # Propagated usage requirements project BayesFilter : usage-requirements ".." # Library headers ar
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www.eeworm.com/read/492929/6414209

m ar_g.m

function results = ar_g(y,nlag,ndraw,nomit,prior,start) % PURPOSE: MCMC estimates Bayesian heteroscedastic AR(k) model % imposing stability restrictions using Gibbs sampling % y
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www.eeworm.com/read/485544/6552639

m demhmc3.m

%DEMHMC3 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampling X
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