The `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.

1 2 | ```
pfNonlinBS(data, particles=500, plot=FALSE)
simNonlin(len)
``` |

`data` |
A vector variable containing the sequence of observations. |

`particles` |
An integer specifying the number of particles. |

`plot` |
A boolean variable describing whether a plot should illustrate the (posterior mean) estimated path along with one and two standard deviation intervals. |

`len` |
The length of data sequence to simulate. |

The `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.

The `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.

Adam M. Johansen and Dirk Eddelbuettel

N. J. Gordon, S. J. Salmond, and A. F. M. Smith. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 140(2):107-113, April 1993.

1 2 | ```
sim <- simNonlin(len=50)
res <- pfNonlinBS(sim$data,particles=500,plot=TRUE)
``` |

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