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Bayesian Change Point Detection in S...

Latterman, Russell,

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Bayesian Change Point Detection in Segmented Multi-Group Autoregressive Moving-average Data for the Study of COVID-19 in Wisconsin /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian Change Point Detection in Segmented Multi-Group Autoregressive Moving-average Data for the Study of COVID-19 in Wisconsin // Russell Latterman.
作者:	Latterman, Russell,
面頁冊數:	1 electronic resource (197 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Mathematics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31294479
ISBN:	9798382727059

Bayesian Change Point Detection in Segmented Multi-Group Autoregressive Moving-average Data for the Study of COVID-19 in Wisconsin /
Latterman, Russell,

Bayesian Change Point Detection in Segmented Multi-Group Autoregressive Moving-average Data for the Study of COVID-19 in Wisconsin /Russell Latterman. - 1 electronic resource (197 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Changepoint detection involves the discovery of abrupt fluctuations in population dynamics over time. We take a Bayesian approach to estimating points in time at which the parameters of an autoregressive moving average (ARMA) change, applying a Markov chain Monte Carlo method. We specifically assume that data may originate from one of two groups. We provide estimates of all multi-group parameters of a model of this form for both simulated and real-world data sets. We include a provision to resolve the problem of confounding ARMA parameter estimates and variance of segment data. We apply our model to identify points in time at which influential events affecting 2020 and 2021 outbreaks of COVID-19 in Waukesha County, Wisconsin, may have occurred.

English

ISBN: 9798382727059Subjects--Topical Terms:

515831
Mathematics.
Subjects--Index Terms:

Autoregressive moving average

Bayesian Change Point Detection in Segmented Multi-Group Autoregressive Moving-average Data for the Study of COVID-19 in Wisconsin /
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653 $a Bayesian approach
653 $a Changepoint detection
653 $a COVID-19
653 $a Gibbs sampler
653 $a Well log data
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