An Urn Model for Cascading Failures on a Lattice

A cascading failure is a failure in a system of interconnected parts, in which the breakdown of one element can lead to the subsequent collapse of the others. The aim of this paper is to introduce a simple combinatorial model for the study of cascading failures. In particular, having in mind particle systems and Markov random fields, we take into consideration a network of interacting urns displaced over a lattice. Every urn is Pólya-like and its reinforcement matrix is not only a function of time (time contagion) but also of the behavior of the neighboring urns (spatial contagion), and of a random component, which can represent either simple fate or the impact of exogenous factors. In this way a non-trivial dependence structure among the urns is built, and it is used to study default avalanches over the lattice. Thanks to its flexibility and its interesting probabilistic properties, the given construction may be used to model different phenomena characterized by cascading failures such as power grids and financial networks.

Estimating the number of motor units using random sums with independently thinned terms

The problem of estimating the numbers of motor units N in a muscle is embedded in a general stochastic model using the notion of thinning from point process theory. In the paper a new moment type estimator for the numbers of motor units in a muscle is denned, which is derived using random sums with independently thinned terms. Asymptotic normality of the estimator is shown and its practical value is demonstrated with bootstrap and approximative confidence intervals for a data set from a 31-year-old healthy right-handed, female volunteer. Moreover simulation results are presented and Monte-Carlo based quantiles, means, and variances are calculated for N in{300,600,1000}.

Ventricular-arterial coupling in a rat model of reduced arterial compliance provoked by hypervitaminosis D and nicotine

The vitamin D(3) and nicotine (VDN) model is one of isolated systolic hypertension (ISH) in which arterial calcification raises arterial stiffness and vascular impedance. The effects of VDN treatment on arterial and cardiac hemodynamics have been investigated; however, a complete analysis of ventricular-arterial interaction is lacking. Wistar rats were treated with VDN (VDN group, n = 9), and a control group (n = 10) was included without the VDN. At week 8, invasive indexes of cardiac function were obtained using a conductance catheter. Simultaneously, aortic pressure and flow were measured to derive vascular impedance and characterize ventricular-vascular interaction. VDN caused significant increases in systolic (138 +/- 6 vs. 116 +/- 13 mmHg, P < 0.01) and pulse (42 +/- 10 vs. 26 +/- 4 mmHg, P < 0.01) pressures with respect to control. Total arterial compliance decreased (0.12 +/- 0.08 vs. 0.21 +/- 0.04 ml/mmHg in control, P < 0.05), and pulse wave velocity increased significantly (8.8 +/- 2.5 vs. 5.1 +/- 2.0 m/s in control, P < 0.05). The arterial elastance and end-systolic elastance rose significantly in the VDN group (P < 0.05). Wave reflection was augmented in the VDN group, as reflected by the increase in the wave reflection coefficient (0.63 +/- 0.06 vs. 0.52 +/- 0.05 in control, P < 0.05) and the amplitude of the reflected pressure wave (13.3 +/- 3.1 vs. 8.4 +/- 1.0 mmHg in control, P < 0.05). We studied ventricular-arterial coupling in a VDN-induced rat model of reduced arterial compliance. The VDN treatment led to development of ISH and provoked alterations in cardiac function, arterial impedance, arterial function, and ventricular-arterial interaction, which in many aspects are similar to effects of an aged and stiffened arterial tree.

Posterior Simulation in the Generalized Linear Model with Semiparmetric Random Effects

Generalized linear mixed models with semiparametric random effects are useful in a wide variety of Bayesian applications. When the random effects arise from a mixture of Dirichlet process (MDP) model, normal base measures and Gibbs sampling procedures based on the Pólya urn scheme are often used to simulate posterior draws. These algorithms are applicable in the conjugate case when (for a normal base measure) the likelihood is normal. In the non-conjugate case, the algorithms proposed by MacEachern and Müller (1998) and Neal (2000) are often applied to generate posterior samples. Some common problems associated with simulation algorithms for non-conjugate MDP models include convergence and mixing difficulties. This paper proposes an algorithm based on the Pólya urn scheme that extends the Gibbs sampling algorithms to non-conjugate models with normal base measures and exponential family likelihoods. The algorithm proceeds by making Laplace approximations to the likelihood function, thereby reducing the procedure to that of conjugate normal MDP models. To ensure the validity of the stationary distribution in the non-conjugate case, the proposals are accepted or rejected by a Metropolis-Hastings step. In the special case where the data are normally distributed, the algorithm is identical to the Gibbs sampler.