16,438 research outputs found
Information in statistical physics
We review with a tutorial scope the information theory foundations of quantum
statistical physics. Only a small proportion of the variables that characterize
a system at the microscopic scale can be controlled, for both practical and
theoretical reasons, and a probabilistic description involving the observers is
required. The criterion of maximum von Neumann entropy is then used for making
reasonable inferences. It means that no spurious information is introduced
besides the known data. Its outcomes can be given a direct justification based
on the principle of indifference of Laplace. We introduce the concept of
relevant entropy associated with some set of relevant variables; it
characterizes the information that is missing at the microscopic level when
only these variables are known. For equilibrium problems, the relevant
variables are the conserved ones, and the Second Law is recovered as a second
step of the inference process. For non-equilibrium problems, the increase of
the relevant entropy expresses an irretrievable loss of information from the
relevant variables towards the irrelevant ones. Two examples illustrate the
flexibility of the choice of relevant variables and the multiplicity of the
associated entropies: the thermodynamic entropy (satisfying the Clausius-Duhem
inequality) and the Boltzmann entropy (satisfying the H-theorem). The
identification of entropy with missing information is also supported by the
paradox of Maxwell's demon. Spin-echo experiments show that irreversibility
itself is not an absolute concept: use of hidden information may overcome the
arrow of time.Comment: latex InfoStatPhys-unix.tex, 3 files, 2 figures, 32 pages
http://www-spht.cea.fr/articles/T04/18
Probabilistic Methodology and Techniques for Artefact Conception and Development
The purpose of this paper is to make a state of the art on probabilistic methodology and techniques for artefact conception and development. It is the 8th deliverable of the BIBA (Bayesian Inspired Brain and Artefacts) project. We first present the incompletness problem as the central difficulty that both living creatures and artefacts have to face: how can they perceive, infer, decide and act efficiently with incomplete and uncertain knowledge?. We then introduce a generic probabilistic formalism called Bayesian Programming. This formalism is then used to review the main probabilistic methodology
and techniques. This review is organized in 3 parts: first the probabilistic models from Bayesian networks to Kalman filters and from sensor fusion to CAD systems, second the inference techniques and finally the learning and model acquisition and comparison methodologies. We conclude with the perspectives of the BIBA project as they rise from this state of the art
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