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Computing the <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\alpha \\beta $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mi>\u03b1<\/mml:mi>\n                    <mml:mi>\u03b2<\/mml:mi>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>-divergence\u2014a family of divergences that includes the Kullback\u2013Leibler divergence and Hellinger distance\u2014between the joint distribution of two decomposable models,\u00a0i.e., chordal Markov networks, can be done in time exponential in the treewidth of these models. Extending this result, we propose an approach to compute the exact <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\alpha \\beta $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mi>\u03b1<\/mml:mi>\n                    <mml:mi>\u03b2<\/mml:mi>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>-divergence between any marginal or conditional distribution of two decomposable models. In order to do so tractably, we provide a decomposition over the marginal and conditional distributions of decomposable models. We then show how our method can be used to analyze distributional changes by first applying it to the benchmark image dataset QMNIST and a dataset containing observations from various areas at the Roosevelt Nation Forest and their cover type. Finally, based on our framework, we propose a novel way to quantify the error in contemporary superconducting quantum computers.<\/jats:p>","DOI":"10.1007\/s10115-024-02191-7","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T16:52:00Z","timestamp":1724431920000},"page":"7527-7556","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Computing marginal and conditional divergences between decomposable models with applications in quantum computing and earth observation"],"prefix":"10.1007","volume":"66","author":[{"given":"Loong Kuan","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geoffrey I.","family":"Webb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel F.","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nico","family":"Piatkowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"2191_CR1","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.neucom.2022.01.075","volume":"481","author":"G Alberghini","year":"2022","unstructured":"Alberghini G, Barbon Junior S, Cano A (2022) Adaptive ensemble of self-adjusting nearest neighbor subspaces for multi-label drifting data streams. 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