DOI: 10.18869/acadpub.jirss/20170601

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Gholami G. On the Bayesian Sequential Change-Point Detection. JIRSS. 2016; 0 :0-0
URL: http://jirss.irstat.ir/article-1-381-en.html

Department of Mathematics, Faculty of Sciences, Urmia University, Iran
Abstract:   (267 Views)

The problems of sequential change-point have several important applications, including quality control, failure detection in industrial, finance and signal detection. We discuss a Bayesian approach in the context of statistical process control: at an unknown time τ, the process behavior changes and the distribution of the data changes from p0 to p1. Two cases are considered: (i) p0 and p1 are fully known, (ii) p0 and p1 belong to the same family of distributions and some θ1 , θ2 parameters are unknown. We present a maximum a posteriori estimate of the Change-point which, for the case (i) can be computed in a sequential manner. In addition, we propose to use the Shiryaev’s loss function. Under this assumption, we define a Bayesian stopping rule. For the Poisson distribution and in two cases (i) and (ii), we obtain results for the conjugate prior. 

     
Type of Study: Original Paper | Subject: 62Jxx: Linear inference, regression
Received: 2016/09/15 | Accepted: 2016/09/15 | Published: 2016/09/15

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