Changeset 693 for library/bdm/estim/particles.h

Timestamp:

11/02/09 17:27:29 (15 years ago)

Author:

mido

Message:

mpdf renamed to pdf in the whole library

Files:

• library/bdm/estim/particles.h (modified) (9 diffs)

library/bdm/estim/particles.h

@@ -36 +36 @@
         Array<vec> &_samples;
         //! Parameter evolution model
-        shared_ptr<mpdf> par;
+        shared_ptr<pdf> par;
         //! Observation model
-        shared_ptr<mpdf> obs;
+        shared_ptr<pdf> obs;
         //! internal structure storing loglikelihood of predictions
         vec lls;
@@ -63 +63 @@
                 resmethod = rm;
         };
-        void set_model ( shared_ptr<mpdf> par0, shared_ptr<mpdf> obs0) {
+        void set_model ( shared_ptr<pdf> par0, shared_ptr<pdf> obs0) {
                 par = par0;
                 obs = obs0;
@@ -105 +105 @@
         /*! configuration structure for basic PF
         \code
-        parameter_pdf   = mpdf_class;         // parameter evolution pdf
-        observation_pdf = mpdf_class;         // observation pdf
+        parameter_pdf   = pdf_class;         // parameter evolution pdf
+        observation_pdf = pdf_class;         // observation pdf
         prior           = epdf_class;         // prior probability density
         --- optional ---
@@ -117 +117 @@
         void from_setting(const Setting &set){
                 BM::from_setting(set);
-                par = UI::build<mpdf>(set,"parameter_pdf",UI::compulsory);
-                obs = UI::build<mpdf>(set,"observation_pdf",UI::compulsory);
+                par = UI::build<pdf>(set,"parameter_pdf",UI::compulsory);
+                obs = UI::build<pdf>(set,"observation_pdf",UI::compulsory);
                 
                 prior_from_set(set);
@@ -196 +196 @@
 
 A composition of particle filter with exact (or almost exact) bayesian models (BMs).
-The Bayesian models provide marginalized predictive density. Internaly this is achieved by virtual class MPFmpdf.
+The Bayesian models provide marginalized predictive density. Internaly this is achieved by virtual class MPFpdf.
 */
 
@@ -206 +206 @@
         Array<BM*> BMs;
 
-        //! internal class for MPDF providing composition of eEmp with external components
+        //! internal class for pdf providing composition of eEmp with external components
 
         class mpfepdf : public epdf  {
@@ -306 +306 @@
         MPF () :  jest (pf,BMs) {};
         //! set all parameters at once
-        void set_parameters ( shared_ptr<mpdf> par0, shared_ptr<mpdf> obs0, int n0, RESAMPLING_METHOD rm = SYSTEMATIC ) {
+        void set_parameters ( shared_ptr<pdf> par0, shared_ptr<pdf> obs0, int n0, RESAMPLING_METHOD rm = SYSTEMATIC ) {
                 pf->set_model ( par0, obs0); 
                 pf->set_parameters(n0, rm );
@@ -341 +341 @@
         \code
         BM              = BM_class;           // Bayesian filtr for analytical part of the model
-        parameter_pdf   = mpdf_class;         // transitional pdf for non-parametric part of the model
+        parameter_pdf   = pdf_class;         // transitional pdf for non-parametric part of the model
         prior           = epdf_class;         // prior probability density
         --- optional ---
@@ -350 +350 @@
         */      
         void from_setting(const Setting &set){
-                shared_ptr<mpdf> par = UI::build<mpdf>(set,"parameter_pdf",UI::compulsory);
-                shared_ptr<mpdf> obs= new mpdf(); // not used!!
+                shared_ptr<pdf> par = UI::build<pdf>(set,"parameter_pdf",UI::compulsory);
+                shared_ptr<pdf> obs= new pdf(); // not used!!
 
                 pf = new PF;