Email: harremoes@ieee.org Mobile: +45 30 32 41 71 Skype: peterharremoes Address: 
Research news Lattice theory of causation I am working on a
larger project where I explore concepts related to cause
and effect using lattice theory. I recently published a
paper entitled Entropy
Inequalities on Lattices where the basic results
on the relation between lattices and extreme polymatroid
functions is described. I am now working on a paper
draft where influence diagrams are described in terms of
lattices. Foundation of quantum theory The paper Entropy on
Spin Factors was published last year. It is part
of a series of papers related to foundation of quantum
theory. At ISIT2017 I gave a talk entitled Information
Theory on Spectral Sets. The results strongly
indicate that information theory is only possible in
mathematical structures that can be represented on
Jordan algebras. There are 5 basic types of Jordan
algebras, and density matrices with complex entries is
the most important in the sense that quantum information
theory is normally represented on this type of algebra.
I guess that many results from quantum information
theory can be generalized to Jordan algebras. This work
follows up on a paper entitled Divergence
and Sufficiency for Convex Optimization that I
recently published in Entropy. MDL I have presented the following paper to ISIT 2018. It extends previous reults on horizont independent MDL by relating it to exactness of saddle point approximations. Tail bounds I published a long paper with various bounds on tail probabilities in terms of the signed loglikelihood function in Kybernetika. It has just been awarded the best paper in Kybernetika 2016. Although the inequalities are quite sharp I am sure even sharper inequalities of these types can be obtained. I think the results can be generalized to cover all exponential families with simple variance functions. Even more general inequalities may exist, but at the moment I don't know how to attack the general problem. 

ResearchMy research is centered
on information theory. One of my interests is how to
use ideas from information theory to derive results
in probability theory. Many of the most important
reslts in probability theory are convergence
theorems, and many of these convergence theorems can
be reformulated so that they state that the entropy
of a system increases to a maximum or that a
divergence converge to a minimum. These ideas are
also relevant in the theory of statistical tests.
Recently I have formalized a method for deriving
Jeffreys prior as the optimal prior using the
minimum description length principle. Erdős number = 3
