Download Applications of Discrete-time Markov Chains and Poisson by Eliane Regina Rodrigues, Jorge Alberto Achcar PDF

By Eliane Regina Rodrigues, Jorge Alberto Achcar

​In this short we think about a few stochastic versions which may be used to check difficulties similar to environmental concerns, specifically, air pollution.  The influence of publicity to air pollution on people's healthiness is a truly transparent and good documented topic. for this reason, it truly is very important to procure how one can expect or clarify the behaviour of pollution mostly. Depending on the kind of query that one is drawn to answering, there are numerous of the way learning that problem. between them we could quote, research of the time sequence of the pollutants' measurements, analysis of the knowledge got at once from the knowledge, for example, day-by-day, weekly or monthly averages and conventional deviations. differently to review the behaviour of toxins typically is through mathematical versions. within the mathematical framework we can have for example deterministic or stochastic versions. the kind of versions that we'll examine during this short are the stochastic ones.​

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In order to do so, we assume a Poisson model for the problem. Poisson processes are a special case of continuous-time Markov chains and also of counting processes (see for instance [47, 71], Grimmett and Stirzaker (1982)). Such processes are ideal for modeling problems involving counting of occurrences of events. Thus for Nt recording the number of times that an event of interest occurred in the time interval [0,t), t ≥ 0, let N = {Nt : t ≥ 0} be a Poisson process. If N is a time homogeneous process, then we denote its rate by λ > 0.

22 64 4 57 38 178 237 β = 1 and we take uniform prior distributions for α and σ . Then, in a second approach, based on the information provided by the complete marginal conditional posterior distribution of the parameters, a uniform distribution with appropriate hyperparameters is considered for β , and Gamma prior distributions with suitable hyperparameters are assigned to α and σ . Therefore, we have two versions of the model—one assuming β = 1 and another assuming β an unknown quantity that needs to be estimated.

Also, consider the last part of the observed data. 4, we have that the last change-point occurs in the transition from the 52nd to the 53rd subinterval. Hence, we take j = 53. 7 × W53. Now, we generate a sample of the random quantity W53 and from that we have a sample of the parameter λ53 . Let λ be the mean of this sample. 17 ppm in the next time subinterval is given by k P(X = k) = λ e− λ , k! k = 0, 1, 2 . . If what we are interested in is estimating the probability of the length of the next inter-surpassing time, given that the present one is of length s, then we have the following.

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