High-dimensional autoregressive point processes model how current events trigger or inhibit future events, such as activity by one member of a social network can affect the future activity of his or her neighbors. While past work has focused on estimating the underlying network structure based solely on the times at which events occur on each node of the network, this paper examines the more nuanced problem of estimating context-dependent networks that reflect how features associated with an event (such as the content of a social media post) modulate the strength of influences among nodes. Specifically, we leverage ideas from compositional time series and regularization methods in machine learning to conduct network estimation for high-dimensional marked point processes. Two models and corresponding estimators are considered in detail: an autoregressive multinomial model suited to categorical marks and a logistic-normal model suited to marks with mixed membership in different categories. Importantly, the logistic-normal model leads to a convex negative log-likelihood objective and captures dependence across categories. We provide theoretical guarantees for both estimators, which we validate by simulations and a synthetic data-generating model. We further validate our methods through two real data examples and demonstrate the advantages and disadvantages of both approaches.
Self-exciting Point Processes: Infections and Implementations
Reinhart的“Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications”（arXiv：1708.02647v1）。我从传染病的传播模型中贡献了一些经验。此外，我还试图补充关于所述模型软件可用性的审查，我认为这对“为新用途铺平道路”至关重要。
Bayesian Analysis of Covariate-Driven Hawkes Processes with Application in Plant Epidemiology
MaIAGE Research Unit, INRAE（MaIAGE研究单元，INRAE）；Université Paris - Saclay（巴黎萨克雷大学）；Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家数字技术研究所）；Plant Protection Department, National Institute of Horticultural Research（果树研究所植物保护部）
An Actor-Critic Algorithm with Function Approximation for Risk Sensitive Cost Markov Decision Processes
Department of Computer Science and Automation, Indian Institute of Science（计算机科学与自动化系，印度科学研究院）；Department of Electrical Engineering, Indian Institute of Technology Bombay（电气工程系，印度理工学院孟买分校）
