blockpfGaussianOpt: Block Sampling Particle Filter (Linear Gaussian Model;...

In RcppSMC: Rcpp Bindings for Sequential Monte Carlo

Block Sampling Particle Filter (Linear Gaussian Model; Optimal Proposal)

Description

The blockpfGaussianOpt function provides a simple example for RcppSMC. It is based on a block sampling particle filter for a linear Gaussian model. This is intended only to illustrate the potential of block sampling; one would not ordinarily use a particle filter for a model in which analytic solutions are available. The 'optimal' block sampler in the sense of Doucet, Briers and Senecal (2006) can be implemented in this case.

The simGaussian function simulates data from the associated linear Gaussian state space model.

Usage

  blockpfGaussianOpt(data, particles=1000, lag=5, plot=FALSE) 
  simGaussian(len)

Arguments

data	A vector variable containing the sequence of observations.
particles	An integer specifying the number of particles.
lag	An integer specifying the length of block to use.
plot	A boolean variable describing whether plot should illustrate the estimated path along with the uncertainty.
len	The length of the data sequence to simulate.

Details

The blockpfGaussianOpt function provides a simple example for RcppSMC. It is based on a simple linear Gaussian state space model in which the state evolution and observation equations are: x(n) = x(n-1) + e(n) and y(n) = x(n) + f(n) where e(n) and f(n) are mutually-independent standard normal random variables. The 'optimal' block-sampling proposal described by Doucet et al (2006) is employed.

The simGaussian function simulates from the same model returning both the state and observation vectors.

Value

The blockpfGaussianOpt function returns a matrix containing the final sample paths and a vector containing their weights. The logarithm of the estimated ratio of normalising constants between the final and initial distributions is also returned.

The simGaussian function returns a list containing the state and data sequences.

Author(s)

Adam M. Johansen and Dirk Eddelbuettel

References

A. Doucet, M. Briers, and S. Senecal. Efficient Block Sampling Strategies for sequential Monte Carlo methods. Journal of Computational and Graphical Statistics, 15(3):693-711, 2006.

Examples

  sim <- simGaussian(len=250)
  res <- blockpfGaussianOpt(sim$data,lag=5,plot=TRUE)

RcppSMC documentation built on March 31, 2023, 10:14 p.m.