tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models
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Emmanuel Bacry | Stéphane Gaïffas | Martin Bompaire | Philip Deegan | Søren Poulsen | E. Bacry | Martin Bompaire | Stéphane Gaïffas | P. Deegan | Søren Poulsen
[1] Yosihiko Ogata,et al. Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes , 1988 .
[2] Emmanuel Bacry,et al. Second order statistics characterization of Hawkes processes and non-parametric estimation , 2014, 1401.0903.
[3] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[4] Erik A. Lewis,et al. RESEARCH ARTICLE A Nonparametric EM algorithm for Multiscale Hawkes Processes , 2011 .
[5] Shai Shalev-Shwartz,et al. Stochastic dual coordinate ascent methods for regularized loss , 2012, J. Mach. Learn. Res..
[6] J. Klein,et al. Statistical Models Based On Counting Process , 1994 .
[7] Emmanuel Bacry,et al. Uncovering Causality from Multivariate Hawkes Integrated Cumulants , 2016, ICML.
[8] Le Song,et al. Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes , 2013, AISTATS.
[9] Tong Zhang,et al. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction , 2013, NIPS.
[10] Gilles Louppe,et al. Independent consultant , 2013 .
[11] E. Bacry,et al. Estimation of slowly decreasing Hawkes kernels: application to high-frequency order book dynamics , 2016 .
[12] Hongyuan Zha,et al. Learning Granger Causality for Hawkes Processes , 2016, ICML.