Extended statistical modeling under symmetry; the link toward quantum mechanics

We derive essential elements of quantum mechanics from a parametric structure extending that of traditional mathematical statistics. The basic setting is a set $\mathcal{A}$ of incompatible experiments, and a transformation group $G$ on the cartesian product $\Pi$ of the parameter spaces of these experiments. The set of possible parameters is constrained to lie in a subspace of $\Pi$, an orbit or a set of orbits of $G$. Each possible model is then connected to a parametric Hilbert space. The spaces of different experiments are linked unitarily, thus defining a common Hilbert space $\mathbf{H}$. A state is equivalent to a question together with an answer: the choice of an experiment $a\in\mathcal{A}$ plus a value for the corresponding parameter. Finally, probabilities are introduced through Born's formula, which is derived from a recent version of Gleason's theorem. This then leads to the usual formalism of elementary quantum mechanics in important special cases. The theory is illustrated by the example of a quantum particle with spin.Comment: The paper has been withdrawn because it is outdate

The quantum formulation derived from assumptions of epistemic processes

Motivated by Quantum Bayesianism I give background for a general epistemic approach to quantum mechanics, where complementarity and symmetry are the only essential features. A general definition of a symmetric epistemic setting is introduced, and for this setting the basic Hilbert space formalism is arrived at under certain technical assumptions. Other aspects of ordinary quantum mechanics will be developed from the same basis elsewhere.Comment: 10 page

Towards quantum mechanics from a theory of experiments

Essential elements of quantum theory are derived from an epistemic point of view, i.e., the viewpoint that thetheory has to do with what can be said about nature. This gives a relationship to statistical reasoning and to other areas of modelling and decision making. In particular, a quantum state can be defined from an epistemic point of view to consist of two elements: A (maximal) question about the value of some parameter together with the answer to that question. Quantization itself can be approached from the point of view of model reduction under symmetry.Comment: Proceedings from QTS-4, Varna, Bulgaria 200

Symmetry in a space of conceptual variables

A conceptual variable is any variable defined by a person or by a group of persons. Such variables may be inaccessible, meaning that they cannot be measured with arbitrary accuracy on the physical system under consideration at any given time. An example may be the spin vector of a particle; another example may be the vector (position, momentum). In this paper, a space of inaccessible conceptual variables is defined, and group actions are defined on this space. Accessible functions are then defined on the same space. Assuming this structure, the basic Hilbert space structure of quantum theory is derived: Operators on a Hilbert space corresponding to the accessible variables are introduced; when these operators have a discrete spectrum, a natural model reduction implies a new model in which the values of the accessible variables are the eigenvalues of the operator. The principle behind this model reduction demands that a group action may also be defined also on the accessible variables; this is possible if the corresponding functions are permissible, a term that is precisely defined. The following recent principle from statistics is assumed: every model reduction should be to an orbit or to a set of orbits of the group. From this derivation, a new interpretation of quantum theory is briefly discussed: I argue that a state vector may be interpreted as connected to a focused question posed to nature together with a definite answer to this question. Further discussion of these topics is provided in a recent book published by the author of this paper

Quantum theory as a statistical theory under symmetry

Both statistics and quantum theory deal with prediction using probability. We will show that there can be established a connection between these two areas. This will at the same time suggest a new, less formalistic way of looking upon basic quantum theory. A total parameter space $\Phi$, equipped with a group $G$ of transformations, gives the mental image of some quantum system, in such a way that only certain components, functions of the total parameter $\phi$ can be estimated. Choose an experiment/ question $a$, and get from this a parameter space $\Lambda^{a}$, perhaps after some model reduction compatible with the group structure. The essentially statistical construction of this paper leads under natural assumptions to the basic axioms of quantum mechanics, and thus implies a new statistical interpretation of this traditionally very formal theory. The probabilities are introduced via Born's formula, and this formula is proved from general, reasonable assumptions, essentially symmetry assumptions. The theory is illustrated by a simple macroscopic example, and by the example of a spin 1/2 particle. As a last example we show a connection to inference between related macroscropic experiments under symmetry.Comment: The paper has been withdrawn because it is outdate

Cross-bispectrum computation and variance estimation

A method for the estimation of cross-bispectra of discrete real time series is developed. The asymptotic variance properties of the bispectrum are reviewed, and a method for the direct estimation of bispectral variance is given. The symmetry properties are described which minimize the computations necessary to obtain a complete estimate of the cross-bispectrum in the right-half-plane. A procedure is given for computing the cross-bispectrum by subdividing the domain into rectangular averaging regions which help reduce the variance of the estimates and allow easy application of the symmetry relationships to minimize the computational effort. As an example of the procedure, the cross-bispectrum of a numerically generated, exponentially distributed time series is computed and compared with theory