Graphical models and in particular Hidden Markov Models or their continuous space equivalent, the so called Kalman filter model, are a powerful tool to make some inference that can be used in decision making contexts. The estimation of their parameters is usually based on the Expectation...

This paper deals with estimating model parameters in graphical models. We reformulate it as an information geometric optimization problem and introduce a natural gradient descent strategy that incorporates additional meta parameters. We show that our approach is a strong alternative to the...

This paper presents a method for visualizing competitive market structures based on scanner panel date where asymmetries are taken into account. For this, I combined consumer choice models based on mixed logit models with three-mode principal component analysis. This approach can be used to...

There is no package in R to plot bivariate distributions for discrete variables or variables given by classes. Therefore, with the help of the already implemented R routine "persp" R functions will be proposed for 3-D plots of the bivariate distribution of discrete variables, the so-called...

Even for a well-trained statistician the construction of a histogram for a given real-valued set is a sifficult problem. It is even more difficult to construct a fully automatic procedure which specifies the number and widths of the binss in a satisfactory manner for a wide range of data sets....

We apply graphical modelling theory to identify fiscal policy shocks in SVAR models of the US economy. Unlike other econometric approaches of which achieve identification by relying on potentially contentious a priori assumptions of graphical modelling is a data based tool. Our results are in...