We discuss the data available for the TSE 300 and S&P 500 total return indexes. We consider a number of models, including the Wilkie model and regime switching models. We discuss calibration by maximum likelihood and by Markov chain Monte Carlo for the regime switching lognormal model. We then show how to use this model to price and hedge simple segregated fund maturity guarantees. keywords: Re...

3d city models are used in a huge number of applications today. They are applicable in the area of urban planning and city development, tourism and marketing and as well for navigation. All these applications need a 3d city model of a large area. And in these days the desire for actuality and a high degree of details is rising. Due to this the modelling of buildings as block models as before is...

In this work, we applied Markov chain modeling to gain insight into the respiratory patterns of extremely preterm newborns being considered for weaning from breathing support (extubation). We show that semi-Markov chains provide more robust modeling capability than Markov chains, especially when dwell time durations in states are very long and are not distributed exponentially. We also introduc...

Hidden Markov models assume a sequence of random variables to be conditionally independent given a sequence of state variables which forms a Markov chain. Maximum-likelihood estimation for these models can be performed using the EM algorithm. In this paper the consistency of a sequence of maximum-likelihood estimators is proved. Also, the conclusion of the Shannon-McMillan-Breiman theorem on en...

We propose in this paper a novel approach to the induction of the structure of Hidden Markov Models. The induced model is seen as a lumped process of a Markov chain. It is constructed to fit the dynamics of the target machine, that is to best approximate the stationary distribution and the mean first passage times observed in the sample. The induction relies on non-linear optimization and itera...

Markov chain theory is a popular statistical tool in applied probability that is quite useful in modelling real-world computing applications. Over the past years; there has been grown interest to employ Markov chain theory in statistical learning of temporal (i.e. time series) data. A wide range of applications found to utilize Markov concepts; such applications include computational linguists,...

The purpose of this paper is to apply and validate an application of Markov chain models to measure the effects of different staffing levels on group performance whilst including the effects of absenteeism. Two models were formulated, one that models absenteeism in detail, and another that uses a simplified approach. Experiments and Monte Carlo simulations were conducted to confirm the validity...

T his paper present s finite Markov chain models of the select ion strategy known as Boltzmann tournament select ion. Unlike previous research at the string level, this st udy represents popul at ions at the more general, equivalence-class level. The changing distribution of classes is analyzed using Markov chains , and a Markov chain model is used to predict expected dr ift t ime for the selec...

CORSIM is a large microsimulator for vehicular traffic, and is being studied with respect to its ability to successfully model and predict behavior of traffic in a 36 block section of Chicago. Inputs to the simulator include information about street configuration, driver behavior, traffic light timing, turning probabilities at each corner and distributions of traffic ingress into the system. Da...