My research is centred 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 lenght principle. I am also interested in quantum information theory, and I think that information theory sheds new light on the problems of the foundation of quantum mechanics. In a sense the distinction between matter and information about matter disappear on the quantum level. Combining this idea with group representations should be a key to a better understanding of quantum theory. I have also worked on the relation between Bayesian networks and irreversibility, and my ultimate goal is to build a bridge between these ideas and information theory. I am working on a new theory where methods from lattice theory are used. I think lattices of functional dependence will provide a more transparent framework to describe causation. Hopefully it will lead to better algorithms for detecting causal relationship, but the most important application might be in our description of quantum systems, where we know that our usual notion of causation break down.
Convexity Decision Theory Inequality Information Theory lattice theory Mathematical Logic, Set Theory, Lattices and Universal Algebra Mathematical Physics Probability Theory Quantum Information Statistics
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