Monte Carlo simulation of the renewal function

1981 ◽  
Vol 18 (02) ◽  
pp. 426-434 ◽  
Author(s):  
Mark Brown ◽  
Herbert Solomon ◽  
Michael A. Stephens
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Renewal Process ◽  
Time Distribution ◽  
Interarrival Time ◽  
Renewal Function ◽  
Expected Number ◽  
Unbiased Estimators ◽  
Monte Carlo Estimation ◽  
Naive Estimator

The problem of Monte Carlo estimation of M(t) = EN(t), the expected number of renewals in [0, t] for a renewal process with known interarrival time distribution F, is considered. Several unbiased estimators which compete favorably with the naive estimator, N(t), are presented and studied. An approach to reduce the variance of the Monte Carlo estimator is developed and illustrated.

Monte Carlo simulation of the renewal function

1981 ◽  
Vol 18 (2) ◽  
pp. 426-434 ◽  
Author(s):  
Mark Brown ◽  
Herbert Solomon ◽  
Michael A. Stephens
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Renewal Process ◽  
Time Distribution ◽  
Interarrival Time ◽  
Renewal Function ◽  
Expected Number ◽  
Unbiased Estimators ◽  
Monte Carlo Estimation ◽  
Naive Estimator

The problem of Monte Carlo estimation of M(t) = EN(t), the expected number of renewals in [0, t] for a renewal process with known interarrival time distribution F, is considered. Several unbiased estimators which compete favorably with the naive estimator, N(t), are presented and studied. An approach to reduce the variance of the Monte Carlo estimator is developed and illustrated.

A general formula for the downtime distribution of a parallel system

1996 ◽  
Vol 33 (03) ◽  
pp. 772-785
Author(s):  
Harald Haukås ◽  
Terje Aven
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Time Distribution ◽  
Life Time ◽  
Exact Formula ◽  
Parallel System ◽  
System Failure ◽  
System Failures ◽  
Failure Scenario ◽  
Common Situation

In this paper we study the problem of computing the downtime distribution of a parallel system comprising stochastically identical components. It is assumed that the components are independent, with an exponential life-time distribution and an arbitrary repair time distribution. An exact formula is established for the distribution of the system downtime given a specific type of system failure scenario. It is shown by performing a Monte Carlo simulation that the portion of the system failures that occur as described by this scenario is close to one when we consider a system with quite available components, the most common situation in practice. Thus we can use the established formula as an approximation of the downtime distribution given system failure. The formula is compared with standard Markov expressions. Some possible extensions of the formula are presented.

A general formula for the downtime distribution of a parallel system

1996 ◽  
Vol 33 (3) ◽  
pp. 772-785 ◽  
Author(s):  
Harald Haukås ◽  
Terje Aven
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Time Distribution ◽  
Life Time ◽  
Exact Formula ◽  
Parallel System ◽  
System Failure ◽  
System Failures ◽  
Failure Scenario ◽  
Common Situation

In this paper we study the problem of computing the downtime distribution of a parallel system comprising stochastically identical components. It is assumed that the components are independent, with an exponential life-time distribution and an arbitrary repair time distribution. An exact formula is established for the distribution of the system downtime given a specific type of system failure scenario. It is shown by performing a Monte Carlo simulation that the portion of the system failures that occur as described by this scenario is close to one when we consider a system with quite available components, the most common situation in practice. Thus we can use the established formula as an approximation of the downtime distribution given system failure. The formula is compared with standard Markov expressions. Some possible extensions of the formula are presented.

PAUSING-TIME DISTRIBUTION OF ANOMALOUS DIFFUSION OF HYDROGEN WITH RANDOM ENERGY

2014 ◽  
Vol 28 (05) ◽  
pp. 1450042 ◽  
Author(s):  
HARUMI HIKITA ◽  
KAZUO MORIGAKI
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Walk ◽  
Power Law ◽  
Time Distribution ◽  
Fractal Structure ◽  
Analytical Calculation ◽  
Disordered System ◽  
Random Energy ◽  
Diffusion Of Hydrogen

It has been empirically known that the disordered system exhibits a power-law behavior. We show from calculation the power law t-1-α for the pausing-time distribution of thermal diffusion of hydrogen in the exponential density of states. This power law of the pausing-time distribution is examined by the Monte Carlo simulation. The results agree with those obtained by analytical calculation. We discuss the power law in terms of random walk in fractal structure (Cantor ensemble).

Two results in the theory of queues

1970 ◽  
Vol 7 (1) ◽  
pp. 219-226 ◽  
Author(s):  
H. Ali
Keyword(s):  
Interarrival Time ◽  
Renewal Function ◽  
Expected Number

SummaryIn this paper it is shown that the distribution of the instant of service of a customer is symmetric as between the distributions of service and interarrival time. Also U(t), the expected number of departures in (0, t), is a delayed renewal function for the GI/M/1 queue.

RENEWAL PROCESS WITH FUZZY INTERARRIVAL TIMES AND REWARDS

2003 ◽  
Vol 11 (05) ◽  
pp. 573-586 ◽  
Author(s):  
RUIQING ZHAO ◽  
BAODING LIU
Keyword(s):  
Renewal Process ◽  
Interarrival Time ◽  
Renewal Theorem ◽  
Expected Number ◽  
Fuzzy Variables ◽  
Numerical Example ◽  
Interarrival Times ◽  
Renewal Reward Theorem

This paper considers a renewal process in which the interarrival times and rewards are characterized as fuzzy variables. A fuzzy elementary renewal theorem shows that the expected number of renewals per unit time is just the expected reciprocal of the interarrival time. Furthermore, the expected reward per unit time is provided by a fuzzy renewal reward theorem. Finally, a numerical example is presented for illustrating the theorems introduced in the paper.

Two results in the theory of queues

1970 ◽  
Vol 7 (01) ◽  
pp. 219-226 ◽  
Author(s):  
H. Ali
Keyword(s):  
Interarrival Time ◽  
Renewal Function ◽  
Expected Number

Summary In this paper it is shown that the distribution of the instant of service of a customer is symmetric as between the distributions of service and interarrival time. Also U(t), the expected number of departures in (0, t), is a delayed renewal function for the GI/M/1 queue.

scholarly journals Monte Carlo simulation and analytic approximation of epidemic processes on large networks

2013 ◽  
Vol 11 (4) ◽  
Author(s):  
Noémi Nagy ◽  
Péter Simon
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Markov Chain ◽  
Continuous Time ◽  
Analytic Approximation ◽  
Continuous Time Markov Chain ◽  
Expected Number ◽  
The Third ◽  
Low Dimensional ◽  
Cycle Graph

AbstractLow dimensional ODE approximations that capture the main characteristics of SIS-type epidemic propagation along a cycle graph are derived. Three different methods are shown that can accurately predict the expected number of infected nodes in the graph. The first method is based on the derivation of a master equation for the number of infected nodes. This uses the average number of SI edges for a given number of the infected nodes. The second approach is based on the observation that the epidemic spreads along the cycle graph as a front. We introduce a continuous time Markov chain describing the evolution of the front. The third method we apply is the subsystem approximation using the edges as subsystems. Finally, we compare the steady state value of the number of infected nodes obtained in different ways.

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

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