SISstep.fading: Sequential Importance Sampling Step for Fading Channels

Description Usage Arguments Value References

View source: R/SMC.r

Description

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

Usage

1
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 Aug. 6, 2020, 5:08 p.m.

Related to SISstep.fading in NTS...