Sequential network change detection with its applications to ad impact relation analysis

DOI10.1007/s10618-013-0338-6zbMath1403.68188OpenAlexW1980384701MaRDI QIDQ1711220

Yu Hayashi, Kenji Yamanishi

Publication date: 17 January 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-013-0338-6

zbMATH Keywords

marketinginformation theoryBayesian networkminimum description length principledynamic model selectionnetwork change detection

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Learning and adaptive systems in artificial intelligence (68T05) Marketing, advertising (90B60)

Related Items (1)

Sequential network change detection with its applications to ad impact relation analysis

Uses Software

• glasso

Cites Work

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• Sparse inverse covariance estimation with the graphical lasso
• Estimating the dimension of a model
• Sequential network change detection with its applications to ad impact relation analysis
• Dynamic Model Selection With its Applications to Novelty Detection
• The performance of universal encoding
• MDL denoising
• On-Line Inference for Multiple Changepoint Problems
• Structural Learning with Time-Varying Components: Tracking the Cross-Section of Financial Time Series
• Probability Inequalities for Sums of Bounded Random Variables
• Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
• A new look at the statistical model identification

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