Mixed Poisson processes are a well known class of point processes derived from (stationary) Poisson processes. In particular they cover cases where the intensity of a Poisson process is unknown but can be assumed to follow a known probability distribution. This situation is common e. g. in insurance mathematics where for instance the number of accident claims in which an individual is involved and which is evolving over some time can in principal be well described by a Poisson process with an individual, yet normally unknown intensity corresponding to the individual's accident proneness. Modelling this intensity as a random variable naturally leads to a mixed model. Usually, an insurance company will have a good estimate of the associated mixing distribution due to its large portfolio of policies.
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Paperback. Etat : new. Paperback. Mixed Poisson processes are a well known class of point processes derived from(stationary) Poisson processes. In particular they cover cases where the intensityof a Poisson process is unknown but can be assumed to follow a known probabilitydistribution. This situation is common e. g. in insurance mathematics where forinstance the number of accident claims in which an individual is involved and whichis evolving over some time can in principal be well described by a Poisson processwith an individual, yet normally unknown intensity corresponding to the individual'saccident proneness. Modelling this intensity as a random variable naturally leadsto a mixed model. Usually, an insurance company will have a good estimate of theassociated mixing distribution due to its large portfolio of policies. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783832519599
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Paperback. Etat : new. Paperback. Mixed Poisson processes are a well known class of point processes derived from(stationary) Poisson processes. In particular they cover cases where the intensityof a Poisson process is unknown but can be assumed to follow a known probabilitydistribution. This situation is common e. g. in insurance mathematics where forinstance the number of accident claims in which an individual is involved and whichis evolving over some time can in principal be well described by a Poisson processwith an individual, yet normally unknown intensity corresponding to the individual'saccident proneness. Modelling this intensity as a random variable naturally leadsto a mixed model. Usually, an insurance company will have a good estimate of theassociated mixing distribution due to its large portfolio of policies. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9783832519599
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