A novel algorithm for the inverse problem in elasticity imaging by means of variational r-adaption

Elasticity imaging or elastography is a powerful technique in medicinal imaging for visualizing the stiffness distribution in soft tissue in vivo. It is motivated by the observation that the stiffness in soft tissue is affected by pathologies in many cases. More precisely, diseased tissue tends to be stiffer than the healthy surrounding tissue. The two steps involved in elasticity imaging are: first deforming the tissue and measuring the displacement field in the region of interest using ultrasound or MRI signals; second calculating the underlying stiffness distribution using an inverse analysis. While in common approaches this inverse analysis is based on minimizing the distance between the measured and the computed deformation field depending only on the unknown stiffness distribution, an additional variation of the underlying finite element discretization is the focus of the present work. In doing so, the triangulation is optimized improving the accuracy of the results and increasing the efficiency of the computational framework. (© 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Reconstruction of Coefficients in Scalar Second‐Order Elliptic Equations from Knowledge of Their Solutions

This paper concerns the reconstruction of possibly complex‐valued coefficients in a second‐order scalar elliptic equation that is posed on a bounded domain from knowledge of several solutions of that equation. We show that for a sufficiently large number of solutions and for an open set of corresponding boundary conditions, all coefficients can be uniquely and stably reconstructed up to a well‐characterized gauge transformation. We also show that in some specific situations, a minimum number of such available solutions equal to $I_n = {1 \over 2}n(n + 3)$ is sufficient to uniquely and globally reconstruct the unknown coefficients. This theory finds applications in several coupled‐physics medical imaging modalities including photo‐acoustic tomography, transient elastography, and magnetic resonance elastography. © 2013 Wiley Periodicals, Inc.     

CHARACTERIZING SPECIES DISTRIBUTIONS BY PRODUCTIVITY AND MORTALITY RATES IN MULTISPECIES MODELS

Abstract The temporal change in species composition is one dimension that may be used to characterize ecosystems. Multispecies interactions and species abundance (or biomass) distributions can be analyzed by an N‐species Lotka–Volterra (LV) system of equations. We derive a statistical approximation to the stationary probability distribution of relative species biomass for a system of interacting species. The distribution is parameterized by the mean and variance of the LV species interaction coefficients and can exhibit a variety of shapes. Given this distribution, one can describe the state of the system, and subsequent perturbations within the system, in terms of shifts in the resulting stable distribution. Theoretical distributions with known properties were compared to distributions estimated from simulations and good agreement was found. Our analytical result could be used to develop “ecosystem indicators” for describing changes in the state of species within an ecosystem.     

Un algorithme dérivé de l'algorithme de Metropolis

Statistical physics and stochastic modelling in economic sciences share the same mathematical bases given by the Gibbs distribution, but system characteristics are different. For instance, an economic system can be described by a Bose–Einstein statistics with few non-degenerate states and an infinitesimal “temperature”; under such conditions, the approximation of the most probable configuration is invalid. Therefore, the calculus of the exact solution needs using a Metropolis algorithm, which estimates a Gibbs distribution. This paper presents a much more efficient algorithm. For small systems, the exact distribution on the canonical set can be computed, and then this distribution is compared to the solutions of the old and new algorithms.     

Diagnostics in Birnbaum–Saunders accelerated life models with an application to fatigue data

In industrial statistics, there is great interest in predicting with precision lifetimes of specimens that operate under stress. For example, a bad estimation of the lower percentiles of a life distribution can produce significant monetary losses to organizations due to an excessive amount of warranty claims. The Birnbaum–Saunders distribution is useful for modeling lifetime data. This is because such a distribution allows us to relate the total time until the failure occurs to some type of cumulative damage produced by stress. In this paper, we propose a methodology for detecting influence of atypical data in accelerated life models on the basis of the Birnbaum–Saunders distribution. The methodology developed in this study should be considered in the design of structures and in the prediction of warranty claims. We conclude this work with an application of the proposed methodology on the basis of real fatigue life data, which illustrates its importance in a warranty claim problem. Copyright © 2012 John Wiley & Sons, Ltd.     

Fast and Accurate Approximation of the Full Conditional for Gamma Shape Parameters

The gamma distribution arises frequently in Bayesian models, but there is not an easy-to-use conjugate prior for the shape parameter of a gamma. This inconvenience is usually dealt with by using either Metropolis–Hastings moves, rejection sampling methods, or numerical integration. However, in models with a large number of shape parameters, these existing methods are slower or more complicated than one would like, making them burdensome in practice. It turns out that the full conditional distribution of the gamma shape parameter is well approximated by a gamma distribution, even for small sample sizes, when the prior on the shape parameter is also a gamma distribution. This article introduces a quick and easy algorithm for finding a gamma distribution that approximates the full conditional distribution of the shape parameter. We empirically demonstrate the speed and accuracy of the approximation across a wide range of conditions. If exactness is required, the approximation can be used as a proposal distribution for Metropolis–Hastings. Supplementary material for this article is available online.     

On the spectral gap in the region of negative pressures

It is shown that, between the values of the activity a = 1 and a < 1, there is a gap, which can be overcome by using additional energy. This energy is defined on the spinodal a = 1 (μ = 0) on the P–Z diagram and gives, in the parastatistical distribution, an additional term of Bose condensate type, which is also preserved for μ < 0. This term is the right-hand side of the Fermi–Dirac distribution. In this paper, it is also shown how to find the “liquid–amorphous body” binodal.     

Inference for the Number of Topics in the Latent Dirichlet Allocation Model via Bayesian Mixture Modeling

In latent Dirichlet allocation, the number of topics, T, is a hyperparameter of the model that must be specified before one can fit the model. The need to specify T in advance is restrictive. One way of dealing with this problem is to put a prior on T, but unfortunately the distribution on the latent variables of the model is then a mixture of distributions on spaces of different dimensions, and estimating this mixture distribution by Markov chain Monte Carlo is very difficult. We present a variant of the Metropolis–Hastings algorithm that can be used to estimate this mixture distribution, and in particular the posterior distribution of the number of topics. We evaluate our methodology on synthetic data and compare it with procedures that are currently used in the machine learning literature. We also give an illustration on two collections of articles from Wikipedia. Supplemental materials for this article are available online.     

Simplification of joint confidence regions for the parameters of the Pareto distribution

The Pareto distribution is an important distribution in statistics, which has been widely used in economics to model the distribution of incomes. Separate interval estimations for parameters of the Pareto distribution have been well established in the literature. For a type-II right censored sample, Chen (Metrika 44:191–197, 1996) proposed a joint confidence region for the parameters. Wu (Comput Stat Data Anal 52:3779–3788, 2008) derived a joint confidence region for any type-II censored sample, but its computation is difficult. Both Wu’s and Chen’s results are simplified in this paper, and some errors are corrected. The methodology used in this article can be applied to other distributions, as long as the underlying distribution can be transformed to the standard exponential distribution in a simple way.     

On the M/G/2 queeing model

The M/G/2 queueing model with service time distribution a mixture of m negative exponential distributions is analysed. The starting point is the functional relation for the Laplace–Stieltjes transform of the stationary joint distribution of the workloads of the two servers. By means of Wiener–Hopf decompositions the solution is constructed and reduced to the solution of m linear equations of which the coefficients depend on the zeros of a polynome. Once this set of equations has been solved the moments of the waiting time distribution can be easily obtained. The Laplace–Stieltjes transform of the stationary waiting time distribution has been derived, it is an intricate expression.     

Foldy–Lax approximation on multiple scattering by many small scatterers

The multiple scattering of time harmonic wave emitted by a localized source through a medium with many scatterers can be approximated by an Foldy–Lax self-consistent system when the relative radius of each scatterer is small and the distribution of scatterers is sparse. The scattering amplitude in the Foldy–Lax self-consistent system will be specified in terms of scatterer volume and scattering strength. By neglecting the self-interaction effect, the difference from the exciting field in the Foldy–Lax formula to the analytic wave field given implicitly by the Lippmann–Schwinger integral equation is compared. An upper bound of the difference is obtained in terms of scaled radius and sparsity of the distribution of the scatterers.     

Computing the noncentral gamma distribution, its inverse and the noncentrality parameter

The noncentral gamma distribution can be viewed as a generalization of the noncentral chi-squared distribution and it can be expressed as a mixture of a Poisson density function with a incomplete gamma function. The noncentral gamma distribution is not available in free conventional statistical programs. This paper aimed to propose an algorithm for the noncentral gamma by combining the method originally proposed by Benton and Krishnamoorthy (Comput Stat Data Anal 43(2):249–267, 2003) for the noncentral distributions with the method of inversion of the distribution function with respect to the noncentrality parameter using Newton–Raphson. The algorithms are available in pseudocode and implemented as R functions. To evaluate the accuracy and speed of computation of the algorithms implemented in R, results of the distribution function, density function, quantiles and noncentrality parameter of the noncentral incomplete gamma and its particular case, the noncentral chi-squared, were obtained for the arguments settings used by Benton and Krishnamoorthy (Comput Stat Data Anal 43(2):249–267, 2003) and Chen (J Stat Comput Simul 75(10):813–829, 2005). The implemented routines performed well and, in general, were as accurate than other approximations. The R package denoted ncg is available to download on the CRAN-R package repository http://cran.r-project.org/.     

Probability distribution fitting of schedule overruns in construction projects

The probability of schedule overruns for construction and engineering projects can be ascertained using a ‘best fit’ probability distribution from an empirical distribution. The statistical characteristics of schedule overruns occurring in 276 Australian construction and engineering projects were analysed. Skewness and kurtosis values revealed that schedule overruns are non-Gaussian. Theoretical probability distributions were then fitted to the schedule overrun data; including the Kolmogorov–Smirnov, Anderson–Darling and Chi-Squared non-parametric tests to determine the ‘Goodness of Fit’. A Four Parameter Burr probability function best described the behaviour of schedule overruns, provided the best overall distribution fit and was used to calculate the probability of a schedule overrun being experienced. The statistical characteristics of contract size and schedule overruns were also analysed, and the Wakeby (<AU$1?m and AU$11–50?m), Three Parameter Log-logistic (AU$1–A$10?m) and Beta (AU$51–A$100?m and >AU$101?m) models provided the best distribution fits and were used to calculate schedule overrun probabilities by contract size.     

A Bayesian Motivated Laplace Inversion for Multivariate Probability Distributions

The paper introduces a recursive procedure to invert the multivariate Laplace transform of probability distributions. The procedure involves taking independent samples from the Laplace transform; these samples are then used to update recursively an initial starting distribution. The update is Bayesian driven. The final estimate can be written as a mixture of independent gamma distributions, making it the only methodology which guarantees to numerically recover a probability distribution with positive support. Proof of convergence is given by a fixed point argument. The estimator is fast, accurate and can be run in parallel since the target distribution is evaluated on a grid of points. The method is illustrated on several examples and compared to the bivariate Gaver–Stehfest method.     

Trajectory Planning and Feedforward Control Design for the Temperature Distribution in a Cuboid

The design of a feedforward tracking controller is considered for the temperature distribution in a cuboid, where the temperature on a single lateral surface can be arbitrarily prescribed by a control input. It is shown that there exists a so–called flat or basic output, which allows to explicitly derive the inverse infinite–dimensional system representation suitable for trajectory planning and for the determination of the respective control input necessary to track the desired trajectory in open–loop. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Arbres de Markov Triplet et fusion de Dempster–Shafer

The hidden Markov chains (HMC) (X,Y) have been recently generalized to triplet Markov chains (TMC), which enjoy the same capabilities of restoring a hidden process X from the observed process Y. The posterior distribution of X can be viewed, in an HMC, as a particular case of the so called “Dempster–Shafer fusion” (DS fusion) of the prior Markov with a probability q defined from the observation Y=y. As such, when we place ourselves in the Dempster–Shafer theory of evidence by replacing the probability distribution of X by a mass function M having an analogous Markov form (which gives again the classical Markov probability distribution in a particular case), the result of DS fusion of M with q generalizes the conventional posterior distribution of X. Although this result is not necessarily a Markov distribution, it has been recently shown that it is a TMC, which renders traditional restoration methods applicable. The aim of this Note is to present some generalizations of the latter result: (i) more general HMCs can be considered; (ii) q, which can possibly be a mass function Q, is itself a result of the DS fusion; and (iii) all these results are finally specified in the hidden Markov trees (HMT) context, which generalizes the HMC one. To cite this article: W. Pieczynski, C. R. Acad. Sci. Paris, Ser. I 336 (2003).     

A Discrete-Time Clark–Ocone Formula and its Application to an Error Analysis

In this paper, we will establish a discrete-time version of Clark(–Ocone–Haussmann) formula, which can be seen as an asymptotic expansion in a weak sense. The formula is applied to the estimation of the error caused by the martingale representation. Throughout, we use another distribution theory with respect to Gaussian rather than Lebesgue measure, which can be seen as a discrete Malliavin calculus.     

A Note on the GI/GI/∞ System with Identical Service and Interarrival-Time Distributions

We study the stationary distribution of the number of busy servers in a GI/GI/∞ system in which the service-time distribution is identical to the interarrival-time distribution, and obtain several representations for the variance. As a result we can verify an expression for the variance, conjectured by Rajaratnam and Takawira (IEEE Trans. Vehicular Technol. 50 (2001) 954–970), when the common distribution of interarrival and service times is a gamma distribution.     

A goodness-of-fit statistic for Pareto-type behaviour

The fit of a statistical model can be visually assessed by inspection of a quantile–quantile or QQ plot. For the strict Pareto distribution, since log-transformed Pareto random variables are exponentially distributed, it is natural to consider an exponential quantile plot based on the log-transformed data. In case the data originate from a Pareto-type distribution, the Pareto quantile plot will be linear but only in some of the largest observations. In this paper we modify the Jackson statistic, originally proposed as a goodness-of-fit statistic for testing exponentiality, in such a way that it measures the linearity of the k largest observations on the Pareto quantile plot. Further, by taking the second-order tail behaviour of a Pareto-type model into account we construct a bias-corrected Jackson statistic. For both statistics the limiting distribution is derived. Next to these asymptotic results we also evaluate the small sample behaviour on the basis of a simulation study. The method is illustrated on two practical case studies.     

General propagation lattice Boltzmann model for variable-coefficient non-isospectral KdV equation

In this paper, a general propagation lattice Boltzmann model for variable-coefficient non-isospectral Korteweg–de Vries (vc-nKdV) equation, which can describe the interfacial waves in a two layer liquid and Alfvén waves in a collisionless plasma, is proposed by selecting appropriate equilibrium distribution function and adding the compensate function. The Chapman–Enskog analysis shows that the vc-nKdV equation can be recovered correctly from the present model. Numerical simulation for the non-propagating one soliton of this equation in different situations is conducted as validation. It is found that the numerical results match well with the analytical solutions, which demonstrates that the current general propagation lattice Boltzmann model is a satisfactory and efficient method, and could be more stable and accurate than the standard lattice Bhatnagar–Gross–Krook model.