Results 1  10
of
72
NonUniform Random Variate Generation
, 1986
"... Abstract. This is a survey of the main methods in nonuniform random variate generation, and highlights recent research on the subject. Classical paradigms such as inversion, rejection, guide tables, and transformations are reviewed. We provide information on the expected time complexity of various ..."
Abstract

Cited by 716 (21 self)
 Add to MetaCart
Abstract. This is a survey of the main methods in nonuniform random variate generation, and highlights recent research on the subject. Classical paradigms such as inversion, rejection, guide tables, and transformations are reviewed. We provide information on the expected time complexity of various algorithms, before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods.
The TimeRescaling Theorem and Its Application to Neural Spike Train Data Analysis
 NEURAL COMPUTATION
, 2001
"... Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model’s validity prior to using it to make inferences about a particular neural system. Assessing goodnessoffit is a challenging problem for point pro ..."
Abstract

Cited by 85 (17 self)
 Add to MetaCart
Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model’s validity prior to using it to make inferences about a particular neural system. Assessing goodnessoffit is a challenging problem for point process neural spike train models, especially for histogrambased models such as perstimulus time histograms (PSTH) and rate functions estimated by spike train smoothing. The timerescaling theorem is a wellknown result in probability theory, which states that any point process with an integrable conditional intensity function may be transformed into a Poisson process with unit rate. We describe how the theorem may be used to develop goodnessoffit tests for both parametric and histogrambased point process models of neural spike trains. We apply these tests in two examples: a comparison of PSTH, inhomogeneous Poisson, and inhomogeneous Markov interval models of neural spike trains from the sup
Discreteevent simulation of Fluid Stochastic Petri Nets
 IEEE Transactions on Software Engineering
, 1999
"... The purpose of this paper is to describe a method for simulation of recently introduced fluid stochastic Petri nets. Since such nets result in rather complex set of partial differential equations, numerical solution becomes a formidable task. Because of a mixed, discrete and continuous state space, ..."
Abstract

Cited by 30 (6 self)
 Add to MetaCart
The purpose of this paper is to describe a method for simulation of recently introduced fluid stochastic Petri nets. Since such nets result in rather complex set of partial differential equations, numerical solution becomes a formidable task. Because of a mixed, discrete and continuous state space, simulative solution also poses some interesting challenges, which are addressed in the paper. 1
Nonparametric estimation of the cumulative intensity function for a nonhomogeneous Poisson process from overlapping realizations
, 2000
"... ..."
Spnp: stochastic petri nets, version 6.0
 in Proc. 2000 Int. Conf. on Computer Performance Evaluation: Modelling Techniques and Tools
"... The Stochastic Petri Net Package (SPNP) [2] is a versatile modeling tool for solution of Stochastic Petri Net (SPN) models. The SPN models are described in the input language for SPNP called CSPL (Cbased SPN Language) which is an extension of the C programming language [8] with additional construct ..."
Abstract

Cited by 13 (0 self)
 Add to MetaCart
The Stochastic Petri Net Package (SPNP) [2] is a versatile modeling tool for solution of Stochastic Petri Net (SPN) models. The SPN models are described in the input language for SPNP called CSPL (Cbased SPN Language) which is an extension of the C programming language [8] with additional constructs which facilitate easy description of SPN models. Moreover, if the user does not want to describe his model in CSPL,
Synchronization of the Neural Response to Noisy Periodic Synaptic Input in a Balanced Leaky IntegrateandFire Neuron with Reversal Potentials
 Neural Computation
, 1999
"... Neurons in which the level of excitation and inhibition are roughly balanced are shown to be very sensitive to the coherence of their synaptic input. The behavior of such balanced neurons with reversal potentials is analyzed both analytically and numerically using the leaky integrateandfire neural ..."
Abstract

Cited by 13 (3 self)
 Add to MetaCart
Neurons in which the level of excitation and inhibition are roughly balanced are shown to be very sensitive to the coherence of their synaptic input. The behavior of such balanced neurons with reversal potentials is analyzed both analytically and numerically using the leaky integrateandfire neural model. The investigation uses the Gaussian approximation with synaptic inputs modeled as inhomogeneous Poisson processes. The results indicate that for balanced neurons with N synaptic inputs, it is only necessary for O( # N) of the synaptic inputs to have a periodicity in order that their spike outputs become phaselocked to this periodic signal.
Modeling users’ mobility among WiFi access points
 In Proceedings of the International Workshop on Wireless Traffic Measurements and Modeling
, 2005
"... Modeling movements of users is important for simulating wireless networks, but current models often do not reflect real movements. Using real mobility traces, we can build a mobility model that reflects reality. In building a mobility model, it is important to note that while the number of handheld ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
Modeling movements of users is important for simulating wireless networks, but current models often do not reflect real movements. Using real mobility traces, we can build a mobility model that reflects reality. In building a mobility model, it is important to note that while the number of handheld wireless devices is constantly increasing, laptops are still the majority in most cases. As a laptop is often disconnected from the network while a user is moving, it is not feasible to extract the exact path of the user from network messages. Thus, instead of modeling individual user’s movements, we model movements in terms of the influx and outflux of users between access points (APs). We first counted the hourly visits to APs in the syslog messages recorded at APs. We found that the number of hourly visits has a periodic repetition of 24 hours. Based on this observation, we aggregated multiple days into a single day by adding the number of visits of the same hour in different days. We then clustered APs based on the different peak hour of visits. We found that this approach of clustering is effective; we ended up with four distinct clusters and a cluster of stable APs. We then computed the average arrival rate and the distribution of the daily arrivals for each cluster. Using a standard method (such as thinning) for generating nonhomogeneous Poisson processes, synthetic traces can be generated from our model. 1
Techniques for the Fast Simulation of Models of Highly Dependable Systems
 IEEE Transactions on Reliability
, 2001
"... this paper, we review some of the importancesampling techniques that have been developed in recent years to e#ciently estimate dependability measures in Markovian and nonMarkovian models of highly dependable systems. 1 Acronyms MTTF Mean time to failure. MTBF Mean time between failures. CTMC ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
this paper, we review some of the importancesampling techniques that have been developed in recent years to e#ciently estimate dependability measures in Markovian and nonMarkovian models of highly dependable systems. 1 Acronyms MTTF Mean time to failure. MTBF Mean time between failures. CTMC Continuoustime Markov chain. DTMC Discretetime Markov chain. GSMP Generalized semiMarkov process. SAVE System AVailability Estimator. CLT Central limit theorem. VRR Variance reduction ratio. TRR Total e#ort reduction ratio. MSDIS Measurespecific dynamic importance sampling. BLBLR Balance over links balanced likelihood ratio. BLBLRC Balance over links balanced likelihood ratio with cuts. 1 INTRODUCTION High dependability requirements of today's critical and/or commercial systems often lead to complicated and costly designs. The ability to predict relevant dependability measures for such complex systems is essential, not only to guarantee hig
Analyzing earthquake clustering features by using stochastic reconstruction
, 2004
"... [1] On the basis of the epidemictype aftershock sequence (ETAS) model and the thinning procedure, this paper gives the method about how to classify the earthquakes in a given catalogue into different clusters stochastically. The key points of this method are the probabilities of one event being tri ..."
Abstract

Cited by 9 (1 self)
 Add to MetaCart
[1] On the basis of the epidemictype aftershock sequence (ETAS) model and the thinning procedure, this paper gives the method about how to classify the earthquakes in a given catalogue into different clusters stochastically. The key points of this method are the probabilities of one event being triggered by another previous event and being a background event. Making use of these probabilities, we can reconstruct the functions associated with the characteristics of earthquake clusters to test a number of important hypotheses about the earthquake clustering phenomena. We applied this reconstruction method to the shallow seismic data in Japan and also to a simulated catalogue. The results show the following assertions: (1) The functions for each component in the formulation of the spacetime ETAS model are good enough as a firstorder approximation for describing earthquake clusters; (2) a background event triggers less offspring in expectation than a triggered event of the same magnitude; (3) the magnitude distribution of the triggered event depends on the magnitude of its direct ancestor; (4) the diffusion of the aftershock sequence is mainly caused by cascades of individual triggering processes, while no evidence shows that each individual triggering process is diffusive;
A Comparative Study of Parallel Algorithms for Simulating Continuous Time Markov Chains
 ACM Trans. Modeling and Computer Simulation
, 1995
"... This paper describes methods for simulating continuous time Markov chain models, using parallel architectures. The basis of our method is the technique of uniformization; within this framework there are a number of options concerning optimism and aggregation. We describe four different variations ..."
Abstract

Cited by 7 (1 self)
 Add to MetaCart
This paper describes methods for simulating continuous time Markov chain models, using parallel architectures. The basis of our method is the technique of uniformization; within this framework there are a number of options concerning optimism and aggregation. We describe four different variations, paying particular attention to an adaptive method that optimistically assumes upper bounds on the rate at which one processor affects another in simulation time, and which recovers from violations of this assumption using global checkpoints. We describe our experiences with these methods on a variety of Intel multiprocessor architectures, including the Touchstone Delta, where excellent speedups of up to 220 using 256 processors are observed. Portions of this paper are reprinted with permission from "Parallel Algorithms for Simulating Continuous Time Markov Chains" in Proceedings of the 1993 Workshop on Parallel and Distributed Simulation, and from "Parallel Simulation of Markovian Que...