SISstep.fading: Sequential Importance Sampling Step for Fading Channels

In NTS: Nonlinear Time Series Analysis

Sequential Importance Sampling Step for Fading Channels

Description

This function implements one step of the sequential importance sampling method for fading channels.

Usage

SISstep.fading(mm, xx, logww, yyy, par, xdim2, ydim)

Arguments

mm	the Monte Carlo sample size m.
xx	the sample in the last iteration.
logww	the log weight in the last iteration.
yyy	the observations with T columns and ydim rows.
par	a list of parameter values. HH is the state coefficient model, WW*t(WW) is the state innovation covariance matrix, VV*t(VV) is the covariance of the observation noise, GG is the observation model.
xdim2	the dimension of the state variable x_t.
ydim	the dimension of the observation y_t.

Value

The function returns a list with the following components:

xx	the new sample.
logww	the log weights.

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.