R code for constructing probability plots

Probability plots are a tool for assessing whether the observed data follow a particular distribution.

probability-plotExample of a probability plot for a Beta distribution

In short, if all data points in a probability plot fall on an approximate straight line, then you may assume that the data fit to the distribution. In the figure above, for instance, all points seem to fall on a straight line in a Beta probability plot. As a result, we may assume that these data points come from a Beta distribution.

The following R code constructs probability plots. Continue reading R code for constructing probability plots

R code for estimating the parameters of a Gauss-Hermite Kalman filter model using likelihood maximization

In one of my previous blog posts I demonstrated how to implement and apply the Gauss-Hermite Kalman Filter (GHKF) in R.
In this post I will show how to fit unknown parameters of a GHKF model by means of likelihood maximization.

Gauss-Hermite Kalman filter Lorenz system

Continue reading R code for estimating the parameters of a Gauss-Hermite Kalman filter model using likelihood maximization

R code for estimating the parameters of an unscented Kalman filter model using likelihood maximization

In one of my previous blog posts I showed how to implement and apply the Unscented Kalman Filter (UKF) in R.
In this post I will demonstrate how to fit unknown parameters of an UKF model by means of likelihood maximization.

Continue reading R code for estimating the parameters of an unscented Kalman filter model using likelihood maximization

R code for estimating the parameters of an extended Kalman filter model using likelihood maximization

In my previous blog post I showed how to implement and use the extended Kalman filter (EKF) in R. In this post I will demonstrate how to fit unknown parameters of an EKF model by means of likelihood maximization.extended Kalman filer MLE Continue reading R code for estimating the parameters of an extended Kalman filter model using likelihood maximization