inference.h File Reference
Header for the model inference tools. More...
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Functions | |
| void | infer_model (Array1D< Array2D< double >(*)(Array2D< double > &, Array2D< double > &, Array2D< double > &, void *) > forwardFuncs, void *funcInfo, string likType, string priorType, double priora, double priorb, Array2D< double > &xdata, Array1D< Array1D< double > > &ydata, Array2D< double > &xgrid, int dataNoiseInference, Array1D< double > &datanoise_array, int pdim, int order, Array1D< int > &rndInd, Array2D< double > &fixIndNom, string pdfType, string pcType, int seed, int nmcmc, double mcmcgamma, bool optimflag, Array1D< double > &chstart, Array1D< double > &chsig, double likParam, int likParam_int, Array2D< double > &pgrid, Array2D< double > &pchain, int nburn, int nstep, Array1D< double > &mapparam, Array1D< double > &datavar_map, Array1D< double > &pmean_map, Array1D< double > &pvar_map, Array1D< double > &fmean_map, Array1D< double > &fvar_map, Array1D< double > &postave_datavar, Array1D< double > &p_postave_mean, Array1D< double > &p_postave_var, Array1D< double > &p_postvar_mean, Array2D< double > &f_postsam_mean, Array1D< double > &f_postave_mean, Array1D< double > &f_postave_var, Array1D< double > &f_postvar_mean, Array2D< double > ¶mPCcfs) |
| Main function for inferring model parameters. | |
| double | LogPosterior (Array1D< double > &m, void *mypost_void) |
| Log-posterior function given a vector of parameters (chain state) and a void* set of auxiliary variables. | |
Detailed Description
Header for the model inference tools.
Function Documentation
◆ infer_model()
| void infer_model | ( | Array1D< Array2D< double >(* | forwardFuncs )(Array2D< double > &, Array2D< double > &, Array2D< double > &, void *) >, |
| void * | funcInfo, | ||
| string | likType, | ||
| string | priorType, | ||
| double | priora, | ||
| double | priorb, | ||
| Array2D< double > & | xdata, | ||
| Array1D< Array1D< double > > & | ydata, | ||
| Array2D< double > & | xgrid, | ||
| int | dataNoiseInference, | ||
| Array1D< double > & | datanoise_array, | ||
| int | pdim, | ||
| int | order, | ||
| Array1D< int > & | rndInd, | ||
| Array2D< double > & | fixIndNom, | ||
| string | pdfType, | ||
| string | pcType, | ||
| int | seed, | ||
| int | nmcmc, | ||
| double | mcmcgamma, | ||
| bool | optimflag, | ||
| Array1D< double > & | chstart, | ||
| Array1D< double > & | chsig, | ||
| double | likParam, | ||
| int | likParam_int, | ||
| Array2D< double > & | pgrid, | ||
| Array2D< double > & | pchain, | ||
| int | nburn, | ||
| int | nstep, | ||
| Array1D< double > & | mapparam, | ||
| Array1D< double > & | datavar_map, | ||
| Array1D< double > & | pmean_map, | ||
| Array1D< double > & | pvar_map, | ||
| Array1D< double > & | fmean_map, | ||
| Array1D< double > & | fvar_map, | ||
| Array1D< double > & | postave_datavar, | ||
| Array1D< double > & | p_postave_mean, | ||
| Array1D< double > & | p_postave_var, | ||
| Array1D< double > & | p_postvar_mean, | ||
| Array2D< double > & | f_postsam_mean, | ||
| Array1D< double > & | f_postave_mean, | ||
| Array1D< double > & | f_postave_var, | ||
| Array1D< double > & | f_postvar_mean, | ||
| Array2D< double > & | paramPCcfs ) |
Main function for inferring model parameters.
- Note
- This is written in a fortran style, i.e. some arguments are inputs, and the rest are output
- Parameters
-
[in] *forwardFuncs : an array of y=f(p,x) functions that take np-by-pdim and nx-by-xdim input arrays and returns an np-by-nx output, see tools/func.h for several examples [in] funcinfo : auxiliary function-specific information (can be 0) [in] likType : likelihood type: options are 'full', 'marg', 'mvn', 'gausmarg', 'abc', 'abcm', 'classical', 'koh' (see UQTk Manual) [in] priorType : prior type: options are 'uniform', 'normal', 'inverse', etc ... (see UQTk Manual) [in] priora,priorb : prior parameters (todo: need to be made dimension-specific) for uniform, it is the range, for normal it is the moments [in] xdata : x-values of data, nx-by-xdim [in] ydata : y-values of data, nx-by-neach [in] xgrid : x-values where predictive moments are computed after the inference : can be the same as xdata [in] dataNoiseInference : indicator, data noise stdev is fixed(0), inferred (1), or log-inferred (2) [in] datanoise_array : data noise stdev array, if fixed, otherwise merely an MCMC starting point [in] pdim : model parameter dimensionality [in] order : order of output PC that is computed via NISP in the likelihood [in] rndInd : array of indices of parameters to be randomized [in] fixIndNom : array of indices and nominal values of parameters to be fixed [in] pdfType : type of PDF PC parameterization, options are 'pct','pci' and 'full' (see UQTk Manual) [in] pcType : type of PC for the PDF parameterization, options are all common PC types, e.g. 'HG','LU' [in] seed : integer seed for MCMC [in] nmcmc : number of MCMC steps to follow optimization; if 0, then only optimization is performed if optimflag is True [in] mcmcgamma : gamma (scaling) parameter for adaptive MCMC [in] optimflag : indicates if optimization is prepended to MCMC [in] chstart : initial chain state [in] chsig : initial non-adaptive, dimensionwise proposal jump size [in] likParam,likParam_int : likelihood parameters (currently, only needed for KDE-based likelihoods, 'full' and 'marg', to pass the KDE bandwidth and number of samples) [in] pgrid : parameter grid, if requested, to compute exact posterior (can be empty) [in] nburn : burn-in for MCMC to write to pchain [in] nstep : thinning of MCMC to write to pchain [in,out] pchain : thinned chain file with nburn and nstep applied, to be used for postprocessing : if given as non-empty array, the MCMC is skipped, and only postprocessing is performed [out] mapparam : MAP parameters [out] datavar_map : MAP value of data variances [out] pmean_map,pvar_map : MAP values of parameter means and variances [out] fmean_map,fvar_map : MAP values of function predition (at xgrid) means and variances [out] postave_datavar : posterior average of data variances [out] p_postave_mean : posterior average of parameter mean [out] p_postave_var : posterior average of parameter variance [out] p_postvar_mean : posterior variance of parameter mean [out] f_postave_mean : posterior average of function prediction mean [out] f_postave_var : posterior average of function prediction variance [out] f_postvar_mean : posterior variance of function prediction mean [out] paramPCcfs : each column is a vector of parameter PC coefficients corresponding to an MCMC sample from pchain the last column is the MAP value of parameter PC coefficients
◆ LogPosterior()
| double LogPosterior | ( | Array1D< double > & | m, |
| void * | mypost_void ) |
Log-posterior function given a vector of parameters (chain state) and a void* set of auxiliary variables.
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