A multi-phase composite with periodic distributed inclusions with a smooth boundary is considered in this contribution. The composite component materials are supposed to be linear viscoelastic and aging (of the non-convolution integral type, for which the Laplace transform with respect to time is not effectively applicable) and are subjected to isotropic shrinkage. The free shrinkage deformation can be considered as a fictitious temperature deformation in the behavior law. The procedure presented in this paper proposes a way to determine average (effective homogenized) viscoelastic and shrinkage (temperature) composite properties and the homogenized stress-field from known properties of the components. This is done by the extension of the asymptotic homogenization technique known for pure elastic non-homogeneous bodies to the non-homogeneous thermo-viscoelasticity of the integral non-convolution type. Up to now, the homogenization theory has not covered viscoelasticity of the integral type. Sanchez-Palencia (1980), Francfort & Suquet (1987) (see , ) have consid- ered homogenization for viscoelasticity of the differential form and only up to the first derivative order. The integral-modeled viscoelasticity is more general then the differential one and includes almost all known differential models. The homogenization procedure is based on the construction of an asymptotic solution with respect to a period of the composite structure. This reduces the original problem to some auxiliary boundary value problems of elasticity and viscoelasticity on the unit periodic cell, of the same type as the original non-homogeneous problem. The existence and uniqueness results for such problems were obtained for kernels satisfying some constrain conditions. This is done by the extension of the Volterra integral operator theory to the Volterra operators with respect to the time, whose 1 kernels are space linear operators for any fixed time variables. Some ideas of such approach were proposed in  and , where the Volterra operators with kernels depending additionally on parameter were considered. This manuscript delivers results of the same nature for the case of the space-operator kernels.
Many discrepancy principles are known for choosing the parameter \(\alpha\) in the regularized operator equation \((T^*T+ \alpha I)x_\alpha^\delta = T^*y^\delta\), \(||y-y^d||\leq \delta\), in order to approximate the minimal norm least-squares solution of the operator equation \(Tx=y\). In this paper we consider a class of discrepancy principles for choosing the regularization parameter when \(T^*T\) and \(T^*y^\delta\) are approximated by \(A_n\) and \(z_n^\delta\) respectively with \(A_n\) not necessarily self - adjoint. Thisprocedure generalizes the work of Engl and Neubauer (1985),and particular cases of the results are applicable to the regularized projection method as well as to a degenerate kernel method considered by Groetsch (1990).
On a family F of probability measures on a measure space we consider the Hellinger and Kullback-Leibler distances. We show that under suitable regulari ty conditions Jeffreys' prior is proportional to the k-dimensional Hausdorff measure w.r.t. Hellinger dis tance respectively to the k2 -dimensional Hausdorff measure w.r.t. Kullback-Leibler distance. The proof i s based on an area-formula for the Hausdorff measure w.r.t. to generalized distances.
A compact subset E of the complex plane is called removable if all bounded analytic functions on its complement are constant or, equivalently, i f its analytic capacity vanishes. The problem of finding a geometric characterization of the removable sets is more than a hundred years old and still not comp letely solved.
Questions arising from Statistical Decision Theory, Bayes Methods and other probability theoretic fields lead to concepts of orthogonality of a family of probability measures. In this paper we therefore give a sketch of a generalized information theory which is very helpful in considering and answering those questions. In this adapted information theory Shannon's classical transition channels modelled by finite stochastic matrices are replaced by compact families of probability measures that are uniformly integrable. These channels are characterized by concepts such as information rate and capacity and by optimal priors and the optimal mixture distribution. For practical studies we introduce an algorithm to calculate the capacity of the whole probability family which is appli cable even for general output space. We then explain how the algorithm works and compare its numerical costs with those of the classical Arimoto-Blahut-algorithm.
It is of basic interest to assess the quality of the decisions of a statistician, based on the outcoming data of a statistical experiment, in the context of a given model class P of probability distributions. The statistician picks a particular distribution P , suffering a loss by not picking the 'true' distribution P' . There are several relevant loss functions, one being based on the the relative entropy function or Kullback Leibler information distance. In this paper we prove a general 'minimax risk equals maximin (Bayes) risk' theorem for the Kullback Leibler loss under the hypothesis of a dominated and compact family of distributions over a Polish observation space with suitably integrable densities. We also find that there is always an optimal Bayes strategy (i.e. a suitable prior) achieving the minimax value. Further, we see that every such minimax optimal strategy leads to the same distribution P in the convex closure of the model class. Finally, we give some examples to illustrate the results and to indicate, how the minimax result reflects in the structure of least favorable priors. This paper is mainly based on parts of this author's doctorial thesis.
Let (Epsilon_k) be a sequence of experiments with the same finite parameter set. Suppose only that identification of the parameter is possible asymptotically. For large classes of information functionals we show that their exponential rates of convergence towards complete information coincide. As a special case we obtain the rate of the Shannon capacity of product experiments.
In this note, answering a question of N. Maslova, we give a two-dimensional elementary example of the phenomenon indicated in the title. Perhaps this simple example may serve as an object of comparison for more refined models like in the theory of kinetic differential equations where similar questions still seem to be unsettled.
The observation of an ergodic Markov chain asymptotically allows perfect identification of the transition matrix. In this paper we determine the rate of the information contained in the first n observations, provided the unknown transition matrix belongs to a known finite set. As an essential tool we prove new refinements of the large deviation theory of the empirical pair measure of finite Markov chains. Keywords: Markov Chain, Entropy, Bayes risk, Large Deviations.