Asymptotic tail behavior of phase-type scale mixture distributions

We consider phase-type scale mixture distributions which correspond to distributions of a product of two independent random variables: a phase-type random variable $Y$ and a nonnegative but otherwise arbitrary random variable $S$ called the scaling random variable. We investigate conditions for such a class of distributions to be either light- or heavy-tailed, we explore subexponentiality and determine their maximum domains of attraction. Particular focus is given to phase-type scale mixture distributions where the scaling random variable $S$ has discrete support --- such a class of distributions has been recently used in risk applications to approximate heavy-tailed distributions. Our results are complemented with several examples.Comment: 18 pages, 0 figur

Spreading Processes over Socio-Technical Networks with Phase-Type Transmissions

Most theoretical tools available for the analysis of spreading processes over networks assume exponentially distributed transmission and recovery times. In practice, the empirical distribution of transmission times for many real spreading processes, such as the spread of web content through the Internet, are far from exponential. To bridge this gap between theory and practice, we propose a methodology to model and analyze spreading processes with arbitrary transmission times using phase-type distributions. Phase-type distributions are a family of distributions that is dense in the set of positive-valued distributions and can be used to approximate any given distributions. To illustrate our methodology, we focus on a popular model of spreading over networks: the susceptible-infected-susceptible (SIS) networked model. In the standard version of this model, individuals informed about a piece of information transmit this piece to its neighbors at an exponential rate. In this paper, we extend this model to the case of transmission rates following a phase-type distribution. Using this extended model, we analyze the dynamics of the spread based on a vectorial representations of phase-type distributions. We illustrate our results by analyzing spreading processes over networks with transmission and recovery rates following a Weibull distribution

Bayesian estimation for the M/G/1 queue using a phase type approximation

This article deals with Bayesian inference and prediction for M/G/1 queueing systems. The general service time density is approximated with a class of Erlang mixtures which are phase type distributions. Given this phase type approximation, an explicit evaluation of measures such as the stationary queue size, waiting time and busy period distributions can be obtained. Given arrival and service data, a Bayesian procedure based on reversible jump Markov Chain Monte Carlo methods is proposed to estimate system parameters and predictive distributions

BAYESIAN ESTIMATION FOR THE M/G/1 QUEUE USING A PHASE TYPE APPROXIMATION

This article deals with Bayesian inference and prediction for M/G/1 queueing systems. The general service time density is approximated with a class of Erlang mixtures which are phase type distributions. Given this phase type approximation, an explicit evaluation of measures such as the stationary queue size, waiting time and busy period distributions can be obtained. Given arrival and service data, a Bayesian procedure based on reversible jump Markov Chain Monte Carlo methods is proposed to estimate system parameters and predictive distributions.

Random point field approach to analysis of anisotropic Bose-Einstein condensations

Position distributions of constituent particles of the perfect Bose-gas trapped in exponentially and polynomially anisotropic boxes are investigated by means of the boson random point fields (processes) and by the spatial random distribution of particle density. Our results include the case of \textit{generalised} Bose-Einstein Condensation. For exponentially anisotropic quasi two-dimensional system (SLAB), we obtain \textit{three} qualitatively different particle density distributions. They correspond to the \textit{normal} phase, the quasi-condensate phase (type III generalised condensation) and to the phase when the type III and the type I Bose condensations co-exist. An interesting feature is manifested by the type II generalised condensation in one-directional polynomially anisotropic system (BEAM). In this case the particle density distribution rests truly random even in the \textit{macroscopic} scaling limit

Viewing the Proton Through "Color"-Filters

While the form factors and parton distributions provide separately the shape of the proton in coordinate and momentum spaces, a more powerful imaging of the proton structure can be obtained through phase-space distributions. Here we introduce the Wigner-type quark and gluon distributions which depict a full-3D proton at every fixed light-cone momentum, like what seen through momentum("color")-filters. After appropriate phase-space reductions, the Wigner distributions are related to the generalized parton distributions (GPD's) and transverse-momentum dependent parton distributions which are measurable in high-energy experiments. The new interpretation of GPD's provides a classical way to visualize the orbital motion of the quarks which is known to be the key to the spin and magnetic moment of the proton.Comment: 4 page

Phase properties of a new nonlinear coherent state

We study phase properties of a displacement operator type nonlinear coherent state. In particular we evaluate the Pegg-Barnett phase distribution and compare it with phase distributions associated with the Husimi Q function and the Wigner function. We also study number- phase squeezing of this state.Comment: 8 eps figures. to appear in J.Opt