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2012 Mar 22
0
New package RcppSMC 0.1.0 for Sequential Monte Carlo and Particle Filters
...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 for
this example by the package.
Two more 'classic' examples from the literature have been added to the
package:
blockpfGaussianOpt() provides the Block Sampling Particle Filter of
Dou...
2012 Mar 22
0
New package RcppSMC 0.1.0 for Sequential Monte Carlo and Particle Filters
...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 for
this example by the package.
Two more 'classic' examples from the literature have been added to the
package:
blockpfGaussianOpt() provides the Block Sampling Particle Filter of
Dou...