64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

Advanced Machine Learning Techniques for General Nonlinear and Non-Gaussian Problems


Ning Ning


  • NN
    Dr Ning Ning

  • NC
    Prof. Nicolas Chopin
  • On the complexity of backward smoothing algorithms

  • DC
    PROF. DR. Dan Crisan
  • Sequential Filtering using Deep Learning

  • JL
    Prof. Jun Liu
  • Modeling, Estimation, and Applications of Generalized Heteroscedastic Gaussian Processes

  • PD
    Pierre Del Moral
  • A theoretical analysis of one-dimensional discrete generation ensemble Kalman particle filters

  • Category: Bernoulli Society for Mathematical Statistics and Probability (BS)


    Sequential Monte Carlo (SMC) as a class of online learning algorithms can handle general non-linear and non-Gaussian modeling and inference. Hence, it has been widely used in signal and image processing, Bayesian inference, risk analysis and rare event sampling, engineering and robotics, bioinformatics, phylogenetics, mathematical finance, etc. This section will present explainable and interpretable SMC algorithms that are suitable for nonlinear and non-Gaussian data science challenges, with rigorous performance guarantees and important scientific applications.