[Rcpp-commits] r3536 - pkg/RcppSMC/inst/announce

[noreply at r-forge.r-project.org]

Author: adamj
Date: 2012-03-20 21:54:57 +0100 (Tue, 20 Mar 2012)
New Revision: 3536

Modified:
   pkg/RcppSMC/inst/announce/RcppSMC-0.1.0.txt
Log:
Slight revision of announcement.


Modified: pkg/RcppSMC/inst/announce/RcppSMC-0.1.0.txt
===================================================================
--- pkg/RcppSMC/inst/announce/RcppSMC-0.1.0.txt	2012-03-20 15:09:05 UTC (rev 3535)
+++ pkg/RcppSMC/inst/announce/RcppSMC-0.1.0.txt	2012-03-20 20:54:57 UTC (rev 3536)
@@ -22,10 +22,11 @@
 By combining the SMCTC with the 'glue' provided by Rcpp, a tighter
 integration with R is achieved.  This allows the applied researcher
 interested in Sequential Monte Carlo and Particle Filter methods to more
-easily vary input data, summarize outputs, plot results and so on.
+easily vary input data, summarize outputs, plot results and so on. 
 
 As a concrete example, figure 5.1 of Johansen (2009) which illustrates a
-Particle Filter for a two-dimensional linear time fit, is reproduced by
+Particle Filter for a two-dimensional linear state space model with
+non-Gaussian observation error, is reproduced by
 
    res <- pfLineartBS(plot=TRUE)
 
@@ -41,15 +42,24 @@
 Two more 'classic' examples from the literature have been added to the
 package:
 
-   blockpfGaussianOpt()   provides the Block Sampling Particle Filter by
-      Doucet, Briers and Senecal (2006, JCGS)
+   blockpfGaussianOpt()   provides the Block Sampling Particle Filter of
+      Doucet, Briers and Senecal (2006, JCGS 15:693) in the context of a 
+      univariate linear Gaussian model.
 
-   pfNonlineBS() provides the Nonlinear Bootstrap Particle Filter by 
-      Gordon, Salmond and Smith (1993, IEE Proceedings-F)
+   pfNonlinBS() provides the Bootstrap Particle Filter of
+      Gordon, Salmond and Smith (1993, IEE Proceedings-F 140:107)
+      in the context of the ubiquitous nonlinear model which was used in
+      section 4.1 of that paper.
 
-We intend to add more example and illustrations over time.  
+These examples are hopefully of some pedagogic interest and provide templates
+which can be used to guide the implementation of more complicated algorithms
+using the Rcpp/SMCTC-combination.
 
+We intend to add more example and illustrations over time and aim ultimately 
+to provide a framework to support the straightforward implementation of SMC
+algorithms which exploits the powerful combination of R and C++.
 
+
 ===== Support =====
 
 Questions about RcppSMC should be directed to the Rcpp-devel mailing list