# SMC.Full.RB: Generic Sequential Monte Carlo Using Full Information... In NTS: Nonlinear Time Series Analysis

 SMC.Full.RB R Documentation

## Generic Sequential Monte Carlo Using Full Information Proposal Distribution and Rao-Blackwellization

### Description

Generic sequential Monte Carlo using full information proposal distribution with Rao-Blackwellization estimate, and delay is 0.

### Usage

``````SMC.Full.RB(
SISstep.Full.RB,
nobs,
yy,
mm,
par,
xx.init,
xdim,
ydim,
resample.sch
)
``````

### Arguments

 `SISstep.Full.RB` a function that performs one step propagation using a proposal distribution. Its input includes `(mm,xx,logww,yyy,par,xdim,ydim,resample)`, where `xx` and `logww` are the last iteration samples and log weight. `yyy` is the observation at current time step. It should return `xx` (the samples xt) and `logww` (their corresponding log weight), `resample` is a binary value for resampling. `nobs` the number of observations `T`. `yy` the observations with `T` columns and `ydim` rows. `mm` the Monte Carlo sample size `m`. `par` a list of parameter values to pass to `Sstep`. `xx.init` the initial samples of `x_0`. `xdim` the dimension of the state varible `x_t`. `ydim` the dimension of the observation `y_t`. `resample.sch` a binary vector of length `nobs`, reflecting the resampling schedule. resample.sch[i]= 1 indicating resample should be carried out at step `i`.

### Value

The function returns a list with the following components:

 `xhat` the fitted values. `xhatRB` the fitted values using Rao-Blackwellization.

### References

Tsay, R. and Chen, R. (2018). Nonlinear Time Series Analysis. John Wiley & Sons, New Jersey.

NTS documentation built on Sept. 25, 2023, 1:08 a.m.