pfNonlinBS
function provides a simple example for
RcppSMC. It is a simple bootstrap particle filter which employs
multinomial resampling after each iteration applied to the ubiquitous "nonlinear
state space model" following Gordon, Salmond and Smith (1993). The simNonlin
function simulates data from the associated model.
pfNonlinBS(data, particles=500, plot=FALSE) simNonlin(len)
pfNonlinBS
function returns two vectors, the first containing the posterior
filtering means; the second the posterior filtering standard deviations.The simNonlin
function returns a list containing the state and data sequences.
pfNonlinbs
function provides a simple example for
RcppSMC. It is based on a simple nonlinear state space model in
which the state evolution and observation equations are:
x(n) = 0.5 x(n-1) + 25 x(n-1) / (1+x(n-1)^2) + 8 cos(1.2(n-1))+ e(n) and
y(n) = x(n)^2 / 20 + f(n)
where e(n) and f(n) are mutually-independent normal random
variables of variances 10.0 and 1.0, respectively. A boostrap proposal
(i.e. sampling from the state equation) is used, together with multinomial
resampling after each iteration. The simNonlin
function simulates from the same model.
sim <- simNonlin(len=50)
res <- pfNonlinBS(sim$data,particles=500,plot=TRUE)
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