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Literature DB >> 23277607

Gaussian processes for time-series modelling.

S Roberts¹, M Osborne, M Ebden, S Reece, N Gibson, S Aigrain.

Abstract

In this paper, we offer a gentle introduction to Gaussian processes for time-series data analysis. The conceptual framework of Bayesian modelling for time-series data is discussed and the foundations of Bayesian non-parametric modelling presented for Gaussian processes. We discuss how domain knowledge influences design of the Gaussian process models and provide case examples to highlight the approaches.

Year: 2012 PMID： 23277607 DOI： 10.1098/rsta.2011.0550

Source DB: PubMed Journal: Philos Trans A Math Phys Eng Sci ISSN： 1364-503X Impact factor: 4.226

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Cited

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6. Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry.

Authors: Wenrong Chen; Elijah N McCool; Liangliang Sun; Yong Zang; Xia Ning; Xiaowen Liu
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Gaussian processes for time-series modelling.

1. Inference for the physical sciences.

2. Anchoring historical sequences using a new source of astro-chronological tie-points.

3. Dynamics of Mechanosensitive Nascent Adhesion Formation.

4. An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data.

5. Context-Aware Time Series Imputation for Multi-Analyte Clinical Data.

6. Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry.

Review 7. Automated Model Inference for Gaussian Processes: An Overview of State-of-the-Art Methods and Algorithms.

8. INFERENCE ABOUT CAUSALITY FROM CARDIOTOCOGRAPHY SIGNALS USING GAUSSIAN PROCESSES.

9. Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals.

10. Bayesian Modeling of Spatial Molecular Profiling Data via Gaussian Process.