Extinction time of the logistic process

The logistic birth and death process is perhaps the simplest stochastic population model that has both density-dependent reproduction and a phase transition, and a lot can be learned about the process by studying its extinction time, $\tau_n$ , as a function of system size n. A number of existing results describe the scaling of $\tau_n$ as $n\to\infty$ for various choices of reproductive rate $r_n$ and initial population $X_n(0)$ as a function of n. We collect and complete this picture, obtaining a complete classification of all sequences $(r_n)$ and $(X_n(0))$ for which there exist rescaling parameters $(s_n)$ and $(t_n)$ such that $(\tau_n-t_n)/s_n$ converges in distribution as $n\to\infty$ , and identifying the limits in each case.

A Demand Prediction Model of Repairable Components based on Grey Birth and Death Process

During the life cycle of equipment, the failure and repair rates of repairable components show uncertain characteristics. The birth and death process (BDP) based on the determined failure and repair rates may not meet the demand forecasting of spare parts. In order to resolve this problem, the grey state transition matrix is constructed by using interval grey numbers to appropriately represent the failure and repair rates of repairable components. In addition, the grey BDP model is built for the demand forecasting of spare parts. The memoryless and existence conditions of steady solution of the grey BDP are studied. To some extent, the spare parts demand law with the uncertain information of the failure and repair rates can easily be revealed. The practical case study is provided to verify the validity and practicability of the proposed model. Also, it provides a new perspective for the spare parts demand prediction problem under the condition of uncertain Markov Process. Accordingly, airlines can predict the maintenance resources demand more accurately and avoid two situations which are not allowed: (1) lower spare parts inventory will lead to the delay production; and (2) higher spare parts inventory will lead to the operating cost pressure.

PREDIKSI PELUANG KEMATIAN DAN KELAHIRAN MURNI PENDUDUK KABUPATEN MERAUKE MENGGUNAKAN METODE BIRTH AND DEATH PROCESS

This study aims to estimate the probability of birth and death purely based on gender and population data of Merauke City. The chance of birth and death will be used to estimate the life table of the elderly in a population of the City of Merauke. The method used in this research is the birth and process method. The Birth and death process method which is a Poisson distribution is used to predict the chances of birth and death at time t. If the birth and death process fulfills the linearity requirements, then the processes are called the Yule-Furry process. This research discusses the stochastic process of pure birth-death with two sexes in the Yule-Furry Process. From the data on the population of Merauke district which is divided based on the sex of men and women using the pure birth and death model, the calculation results show that the probability value at the time interval 0 ≤ t <1 hour, at the initial time t = 0, the chance of individual birth at female sex is stationary at a value of 0.1762, while the chance of individual death for female sex is stationary at a value of 0.00154. The odds of birth and death in male individuals are stationary at a value of 0.305034 and 0, 059487.

A three-term recurrence relation for accurate evaluation of transition probabilities of the simple birth-and-death process

The simple (linear) birth-and-death process is a widely used stochastic model for describing the dynamics of a population. When the process is observed discretely over time, despite the large amount of literature on the subject, little is known about formal estimator properties. Here we will show that its application to observed data is further complicated by the fact that numerical evaluation of the well-known transition probability is an ill-conditioned problem. To overcome this difficulty we will rewrite the transition probability in terms of a Gaussian hypergeometric function and subsequently obtain a three-term recurrence relation for its accurate evaluation. We will also study the properties of the hypergeometric function as a solution to the three-term recurrence relation. We will then provide formulas for the gradient and Hessian of the log-likelihood function and conclude the article by applying our methods for numerically computing maximum likelihood estimates in both simulated and real dataset.

A MATHEMATICAL MODEL OF DEMAND MANAGEMENT FOR CARSHARING

A significant increase of carsharing service in Russia and worldwide has led to need a formal description of the process orders fulfilling on the service. The paper presents a mathematical model for computation the distance between customers and free car, which is built on the basis of the “nearest neighbor”method. A model of demand generation is proposed. The demand depends on the potential number of customers and the probability of finding a car in the area of walking distance. The number of free cars is described as stochastic process, which is based on a birth and death process. The death intensity in the process depends on the probability of finding a car. The estimation of the average number of free cars, their average load and the probability of finding a free car in the walking distance are obtained. The combination of the presented models allows to assess the impact of the number of carsharing vehicles on demand. In particular, a numerical example shows that an increase in the carsheribg fleet can lead to increaseasing in the cars average load. Thus, we can conclude that a larger car-sharing project can be cost-effective only if the carsharing service consists of a large fleet of vehicles, i.e. the service contains a high density of free cars.

Über die Wahrscheinlichkeit des Aussterbens einer Population in einer langsamen periodischen Umgebung

International audience Wir studieren die Wahrscheinlichkeit des Aussterbens eines linearen Geburts- und Todesprozesses mit mehreren Typen in einer periodischen Umgebung, wenn die Periode groß ist. Diese Wahrscheinlichkeit hängt von der Jahreszeit ab und zeigt eine Diskontinuität im Zusammenhang mit einem "Canard" in einem langsam-schnellen dynamischen System. Der Diskontinuitätspunkt wird in einem Beispiel mit zwei Typen genau bestimmt. Dieses Beispiel kommt von einem Modell für eine Krankheit, die durch Vektoren übertragen wird. We study the probability of extinction of a population modelled by a linear birth-and-death process with several types in a periodic environment when the period is large compared to other time scales. This probability depends on the season and may present a sharp jump in relation to a "canard" in a slow-fast dynamical system. The point of discontinuity is determined precisely in an example with two types of individuals related to a vector-borne disease transmission model. On s'intéresse à la probabilité d'extinction d'un processus linéaire de naissance et de mort avec plusieurs types dans un environnement périodique dans la limite d'une période très grande. Cette probabilité dépend de la saison et peut présenter à la limite une discontinuité en lien avec un canard dans un système dynamique lent-rapide. On détermine précisément le point de discontinuité dans un exemple avec deux types d'individus provenant d'un modèle de transmission d'une maladie à vecteurs.

Network inference from population-level observation of epidemics

Using the continuous-time susceptible-infected-susceptible (SIS) model on networks, we investigate the problem of inferring the class of the underlying network when epidemic data is only available at population-level (i.e., the number of infected individuals at a finite set of discrete times of a single realisation of the epidemic), the only information likely to be available in real world settings. To tackle this, epidemics on networks are approximated by a Birth-and-Death process which keeps track of the number of infected nodes at population level. The rates of this surrogate model encode both the structure of the underlying network and disease dynamics. We use extensive simulations over Regular, Erdős–Rényi and Barabási–Albert networks to build network class-specific priors for these rates. We then use Bayesian model selection to recover the most likely underlying network class, based only on a single realisation of the epidemic. We show that the proposed methodology yields good results on both synthetic and real-world networks.