# SMC.Smooth: Generic Sequential Monte Carlo Smoothing with Marginal... In NTS: Nonlinear Time Series Analysis

## Description

Generic sequential Monte Carlo smoothing with marginal weights.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```SMC.Smooth( SISstep, SISstep.Smooth, nobs, yy, mm, par, xx.init, xdim, ydim, resample.sch, funH = identity ) ```

## Arguments

 `SISstep` a function that performs one propagation step using a proposal distribution. Its input includes `(mm,xx,logww,yyy,par,xdim,ydim)`, 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). `SISstep.Smooth` the function for backward smoothing step. `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. `xx.init` the initial samples of `x_0`. `xdim` the dimension of the state variable `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`. `funH` a user supplied function `h()` for estimation `E(h(x_t) | y_1,...,y_T`). 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 the smoothed values.

## References

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

NTS documentation built on Aug. 6, 2020, 5:08 p.m.