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Multiple change point detection and validation in autoregressive time series data
Abstract: It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments between these change points is of interest. In this paper...
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Multiple change point detection and validation in autoregressive time series data.
It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments between these change points is of interest. In this paper, time series...
Published by:
Multiple change point detection and validation in autoregressive time series data
Abstract: It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments between these change points is of interest. In this paper...
Published by:
Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence.
Mesoscale eddies have strong signatures in sea surface height (SSH) anomalies that are measured globally through satellite altimetry. However, monitoring the transport of heat associated with these eddies and its impact on the...
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Conserved cis-regulatory modules control robustness in Msx1 expression at single cell resolution

The process of transcription is highly stochastic leading to cell-to-cell variations and noise in gene expression levels. However, key essential genes have to be precisely expressed at the correct amount and time to ensure...

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Conserved cis-regulatory modules control robustness in Msx1 expression at single cell resolution

The process of transcription is highly stochastic leading to cell-to-cell variations and noise in gene expression levels. However, key essential genes have to be precisely expressed at the correct amount and time to ensure...

Published by: