贝叶斯非参数性模型的matlab代码(Matlab codes for Bayesian nonparametric model)

贝叶斯非参数性模型的matlab代码(Matlab codes for Bayesian nonparametric model)
数据介绍：
Matlab codes for implementing the Bayesian nonparametric model are given and also can be found on our Web site at
(/st1sak). Here is a description of the programs and how they are to be used. Note that these codes are not general and so the user needs to modify them for his or her own purposes.
关键词：
算法,统计,matlab代码,贝叶斯模型,非参数性,
algorithm,statistic,matlab code,Bayesian model,nonparametric,
数据格式：
TEXT
数据详细介绍：
Matlab codes for Bayesian nonparametric mode
Matlab codes for implementing the Bayesian nonparametric model are given and also can be found on our Web site at (/st1sak). Here is a description of the programs and how they are to be used. Note that these
codes are not general and so the user needs to modify them for his or her own purposes.
# Important things to be defined before sourcing the main program metropolisgibbs.m:
1. Supply p-vector of initial values for the parameters (u, alpha, beta,
gama, Vsquared, sigmasquared, tausquared) and call them u0, alpha0, beta0, gama0, Vsquared0, sigmasquared0 and tausquared0.
2. Define data
As already mentioned in the paper, the data comprise individual elicited utilities yij, the corresponding health states xij, individual and health
states counts.
(i) Let yhs be the data set, indiv be the number of individuals and both
sorted according to health states being in an ascending order. ypat be
the same data set, HS be the number of health states and both sorted
according to patients being in an ascending order. NoPPHS (Number of Patients Per Health State) is the number of different respondents who
valued the same health state. NoHSPP (Number of Health States Per
Patient) is the number of different health states valued by the same
patient
3. Define priors
umeanvar and MtMymatrix files return the prior distribution of u.
conditionalphi, metropolis and alphaiterate files create alpha sample
using a random walk Metropolis. betameanvar file returns the prior
distribution of beta. Gama0 = 0, as covariate are not included here.
Vsquaredab, sigmasquaredab and tausquaredab files return the prior
distributions of Vsquared, sigmasquared and tausquared respectively.
4. Define A to be the matrix of covariances and this is obtained from
Amatrix file and H = (1 x) to be the matix of health states
Defining all of the above, you should now be able to run
metropolisgibbs.m file and get the Markov chain Monte Carlo sample of interest. After obtaing this sample, use predictusmeanvar file to
compute the utilities of interest.
5. Finally make use of covariancestuff, predictbetasmeanvar,
extrausmean and extrausvar to predict new health state valuations
outside of the given data set.
6. To this end, residuals file is ready to obtain the residuals.
数据预览：
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