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This construction may be inadequate when data are subject to local correlation patterns, often resulting in many more changepoints fitted than preferable. This article proposes a Bayesian changepoint model that relaxes the assumption of exchangeability within segments. The proposed model supposes data within a segment are<jats:italic>m<\/jats:italic>-dependent for some unknown<jats:inline-formula><jats:alternatives><jats:tex-math>$$m \\geqslant 0$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>m<\/mml:mi><mml:mo>\u2a7e<\/mml:mo><mml:mn>0<\/mml:mn><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>that may vary between segments, resulting in a model suitable for detecting clear discontinuities in data that are subject to different local temporal correlations. The approach is suited to both continuous and discrete data. A novel reversible jump Markov chain Monte Carlo algorithm is proposed to sample from the model; in particular, a detailed analysis of the parameter space is exploited to build proposals for the orders of dependence. Two applications demonstrate the benefits of the proposed model: computer network monitoring via change detection in count data, and segmentation of financial time series.<\/jats:p>","DOI":"10.1007\/s11222-022-10176-1","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T07:03:52Z","timestamp":1668582232000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Changepoint detection in non-exchangeable data"],"prefix":"10.1007","volume":"32","author":[{"given":"Karl L.","family":"Hallgren","sequence":"first","affiliation":[]},{"given":"Nicholas A.","family":"Heard","sequence":"additional","affiliation":[]},{"given":"Niall M.","family":"Adams","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"issue":"1","key":"10176_CR1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/07350015.1993.10509929","volume":"11","author":"JH Albert","year":"1993","unstructured":"Albert, J.H., Chib, S.: Bayes inference via Gibbs sampling of autoregressive time series subject to Markov mean and variance shifts. 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