Lebesgue's decomposition shows that a probability distribution can be decomposed into absolute continuous, discrete, and singular-continuous parts. But in literature, different terminologies are used, which I want to unreval in what follows:
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Currently, I am with Frequentis AG. I have been a consultant and a service provider in information technology with focus on statistics, programming, communications, and streaming of live events, which reflect my interests and hobbies.
From mid-2008 to mid-2009, I did my master thesis on RFID with NXP Semiconductors Austria. Afterwards, I was researcher in statistics / signal processing and teaching assistant with Institute of Telecommunications of Vienna University of Technology. From autumn of 2013 to spring of 2014, I did my research at Institute of Telecommunications of TU Darmstadt due to a fellowship of Federation of Austrian Industries. Afterward I was self-employed and became a certified project-management associate. See my LinkedIn profile or contact me for my detailed curriculum vitae.
My research's foci are statistics, signal processing, and probability theory in the area of (mobile) communications. I did my work in cooperation with Prof. Christoph Mecklenbräuker (PhD advisor), Prof. Peter Gerstoft, Prof. Gerald Matz, Prof. Marius Pesavento, and Prof. Norbert Goertz.
If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is.
John von Neumann (1903-1957)
Two traditional classes of inverse problems are the estimation of absolute-continuous random parameters and the detection/classification of discrete random parameters by continuous random measurements. But what about the inference of mixed discrete-continuous problems? In the following, I will summarize my proposal of six classes of inverse problems and, hence, six classes of inferrers. See  for a table and formulars.