Displaying 2 results from an estimated 2 matches for "pflineartbs".
2012 Mar 22
0
New package RcppSMC 0.1.0 for Sequential Monte Carlo and Particle Filters
...cle Filter methods to more
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 state space model with
non-Gaussian observation error, is reproduced by
res <- pfLineartBS(plot=TRUE)
where we select the optional plot. Moreover, progress during the model fit
can also be visualized (using callbacks into R from C++ which Rcpp provides)
via
res <- pfLineartBS(onlinePlot=pfLineartBSOnlinePlot)
where pfLineartBSOnlinePlot() is a default plotting function provided...
2012 Mar 22
0
New package RcppSMC 0.1.0 for Sequential Monte Carlo and Particle Filters
...cle Filter methods to more
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 state space model with
non-Gaussian observation error, is reproduced by
res <- pfLineartBS(plot=TRUE)
where we select the optional plot. Moreover, progress during the model fit
can also be visualized (using callbacks into R from C++ which Rcpp provides)
via
res <- pfLineartBS(onlinePlot=pfLineartBSOnlinePlot)
where pfLineartBSOnlinePlot() is a default plotting function provided...