# smoother: Kalman Smoothing In bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

 fast_smoother R Documentation

## Kalman Smoothing

### Description

Methods for Kalman smoothing of the states. Function fast_smoother computes only smoothed estimates of the states, and function smoother computes also smoothed variances.

### Usage

fast_smoother(model, ...)

## S3 method for class 'lineargaussian'
fast_smoother(model, ...)

smoother(model, ...)

## S3 method for class 'lineargaussian'
smoother(model, ...)


### Arguments

 model Model to be approximated. Should be of class bsm_ng, ar1_ng svm, ssm_ung, or ssm_mng, or ssm_nlg, i.e. non-gaussian or non-linear bssm_model. ... Ignored.

### Details

For non-Gaussian models, the smoothing is based on the approximate Gaussian model.

### Value

Matrix containing the smoothed estimates of states, or a list with the smoothed states and the variances.

### Examples

model <- bsm_lg(Nile,
sd_level = tnormal(120, 100, 20, min = 0),
sd_y = tnormal(50, 50, 25, min = 0),
a1 = 1000, P1 = 200)
ts.plot(cbind(Nile, fast_smoother(model)), col = 1:2)
model <- bsm_lg(Nile,
sd_y = tnormal(120, 100, 20, min = 0),
sd_level = tnormal(50, 50, 25, min = 0),
a1 = 1000, P1 = 500^2)

out <- smoother(model)
ts.plot(cbind(Nile, out$alphahat), col = 1:2) ts.plot(sqrt(out$Vt[1, 1, ]))


bssm documentation built on May 4, 2022, 1:06 a.m.