Corpus ID: 73729254

Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes

@article{Yue2019VariationalIO,
  title={Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes},
  author={Xubo Yue and Raed Kontar},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.03867}
}
  • Xubo Yue, Raed Kontar
  • Published 2019
  • Mathematics, Computer Science
  • ArXiv
  • We present a non-parametric prognostic framework for individualized event prediction based on joint modeling of both longitudinal and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of longitudinal signals and a Cox model to map time-to-event data with longitudinal data modeled through the MGCP. Taking advantage of the unique structure imposed by convolved processes, we provide a variational inference framework to… CONTINUE READING

    Figures and Topics from this paper.

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 42 REFERENCES
    Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction
    26
    Variational Inference for Gaussian Process Models for Survival Analysis
    3
    Heterogeneous Multi-output Gaussian Process Prediction
    21
    Gaussian Processes for Survival Analysis
    37
    Variational Dependent Multi-output Gaussian Process Dynamical Systems
    22
    Joint models with multiple longitudinal outcomes and a time-to-event outcome: a corrected two-stage approach
    3
    Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues
    78
    Bayesian Gaussian Process Latent Variable Model
    321
    Joint Modeling of Longitudinal and Survival Data
    79
    Multitask Gaussian Processes for Multivariate Physiological Time-Series Analysis
    88