Changeset 693 for library/doc/tutorial/01userguide.dox

Timestamp:

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

Author:

mido

Message:

mpdf renamed to pdf in the whole library

Files:

• library/doc/tutorial/01userguide.dox (modified) (5 diffs)

library/doc/tutorial/01userguide.dox

@@ -162 +162 @@
      - bdm::CsvFileDS
      - bdm::ITppFileDS
- - bdm::MpdfDS
+ - bdm::PdfDS
  - bdm::stateDS
 
@@ -243 +243 @@
 fu.R     = 0.2;
 
-DS.class = 'MpdfDS';
-DS.mpdf.class  = 'mprod';
-DS.mpdf.mpdfs  = {fy, epdf2mpdf(fu)};
+DS.class = 'pdfDS';
+DS.pdf.class  = 'mprod';
+DS.pdf.pdfs  = {fy, epdf2mpdf(fu)};
 \endcode
 
@@ -252 +252 @@
  - naming convention 'mlnorm\<ldmat\>' relates to the concept of templates in C++. For those unfamiliar with this concept, it is basicaly a way how to share code for different flavours of the same object. Note that mlnorm exist in three versions: mlnorm\<ldmat\>, mlnorm<chmat>, mlnorm<fsqmat>. Those classes act identically the only difference is that the internal data are stored either in LD decomposition, choleski decomposition or full matrices, respectively. 
  - the same concept is used for enorm, where enorm<chmat> and enorm<fsqmat> are also possible. In this particular use, these objects are equivalent. In specific situation, e.g. Kalman filter implemented on Choleski decomposition (bdm::KalmanCh), only enorm<chmat> is approprate.
- - class 'mprod' represents the chain rule of probability. Attribute \c mpdfs of its configuration structure is a list of conditional densities. Conditional density \f$ f(a|b)\f$ is represented by class \c mpdf and its offsprings. Class \c RV is used to describe both variables before conditioning (field \c rv ) and after conditioning sign (field \c rvc).
- - due to simplicity of implementation, mprod accept only conditional densities in the field \c mpdfs. Hence, the pdf \f$ f(u_t)\f$ must be converted to conditional density with empty conditioning, \f$ f(u_t| \{\})\f$. This is achieved by calling function epdf2mpdf which is only a trivial wrapper creating class bdm::mepdf.
+ - class 'mprod' represents the chain rule of probability. Attribute \c pdfs of its configuration structure is a list of conditional densities. Conditional density \f$ f(a|b)\f$ is represented by class \c pdf and its offsprings. Class \c RV is used to describe both variables before conditioning (field \c rv ) and after conditioning sign (field \c rvc).
+ - due to simplicity of implementation, mprod accept only conditional densities in the field \c pdfs. Hence, the pdf \f$ f(u_t)\f$ must be converted to conditional density with empty conditioning, \f$ f(u_t| \{\})\f$. This is achieved by calling function epdf2mpdf which is only a trivial wrapper creating class bdm::mepdf.
  
  
@@ -260 +260 @@
 \subsection ug_ini Initializing simulation
 
-When zeros are not appropriate initial conditions, the correct conditions can be set using additional commands (see bdm::MpdfDS.from_setting() ):
+When zeros are not appropriate initial conditions, the correct conditions can be set using additional commands (see bdm::pdfDS.from_setting() ):
 \code
 DS.init_rv = RV({'y','y','y'}, [1,1,1], [-1,-2,-3]);
@@ -281 +281 @@
 Data=[M.y; M.u];
 drv = RVjoin({y,u});
-save mpdfds_results Data drv
+save pdfds_results Data drv
 \endcode
 Such data can be later provided e.g. by MemDS