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

 SMC.Full R Documentation

## Generic Sequential Monte Carlo Using Full Information Proposal Distribution

### Description

Generic sequential Monte Carlo using full information proposal distribution.

### Usage

``````SMC.Full(
SISstep.Full,
nobs,
yy,
mm,
par,
xx.init,
xdim,
ydim,
resample.sch,
delay = 0,
funH = identity
)
``````

### Arguments

 `SISstep.Full` 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`. `delay` the maximum delay lag for delayed weighting estimation. Default is zero. `funH` a user supplied function `h()` for estimation `E(h(x_t) | y_t+d`). Default is identity for estimating the mean. The function should be able to take vector or matrix as input and operates on each element of the input.

### Value

The function returns a list with the following components:

 `xhat` the fitted values. `loglike` the log-likelihood.

### 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.