Changeset 714

Timestamp:

11/10/09 11:43:14 (14 years ago)

Author:

mido

Message:

_m changed to _mat even in documentation

Files:

• library/doc/local/Intro.dox (modified) (2 diffs)

library/doc/local/Intro.dox

@@ -50 +50 @@
 This class allows such as moment evaluation via methods \c mean() and \c variance(), marginalization via method \c marginal(), and conditioning via method \c condition(). 
 
-Also, it allows generation of a sample via \c sample() and evaluation of one value of the posterior parameter likelihood via \c evallog(). Multivariate versions of these operations are also available by adding suffix \c _m, i.e. \c sample_m() and \c evallog_m().  These methods providen multiple samples and evaluation of likelihood in multiple points respectively. 
+Also, it allows generation of a sample via \c sample() and evaluation of one value of the posterior parameter likelihood via \c evallog(). Multivariate versions of these operations are also available by adding suffix \c _mat, i.e. \c sample_mat() and \c evallog_mat().  These methods providen multiple samples and evaluation of likelihood in multiple points respectively. 
 
 \section pc Classes for probability calculus
@@ -59 +59 @@
 <li> \c RV a symbolic variable on which pdfs are defined.</li>
 </ul>
-The former class is an extension of mpdf that allows conditioning on a symbolic variable. Hence, when numerical results - such as samples -  are required, numericla values of the condition must be provided. The names of methods of the \c epdf are used extended by suffix \c cond, i.e. \c samplecond(), \c evallogcond(), where \c cond precedes matrix estension, i.e. \c samplecond_m() and \c evallogcond_m().
+The former class is an extension of mpdf that allows conditioning on a symbolic variable. Hence, when numerical results - such as samples -  are required, numericla values of the condition must be provided. The names of methods of the \c epdf are used extended by suffix \c cond, i.e. \c samplecond(), \c evallogcond(), where \c cond precedes matrix estension, i.e. \c samplecond_mat() and \c evallogcond_mat().
 
 The latter class is used to identify how symbolic variables are to be combined together. For example, consider the task of composition of pdfs via the chain rule: