Random Measures: Asymptotics, Bayesian Inference, and Stochastic Dynamics

随机测度是测度值随机元。随机测度的集合成为测度值过程。随机测度和测度值过程在过去的三十年里一直是概率论、随机过程和统计学中非常活跃的研究课题。一些研究得很好的模型包括随机分配，聚结，狄利克雷过程，棒断裂模型，弗莱明-维奥特过程，和各种瓮模型。他们研究的一个主要动机来自于对复杂的相互作用系统和相应的演化动力学进行建模。在这一领域的深入研究导致了概率论和随机过程的许多理论进展，以及天体物理学，化学，通信，生态学，经济学，语言学，机器学习和人口遗传学的大量应用。 迄今为止取得的进展和进步是非常显著的。但仍存在许多挑战。首先，许多随机测量和过程依赖于未知参数。它们在各种参数区域的渐近行为是重要的特征，并且大多是未知的。其次，在随机动力学的研究中，不仅对模型更接近实际有强烈的需求，而且对数据拟合的校准方法也有明确的需求。需要开发更有效的统计方法。第三，数学模型如聚结模型已被用于研究进化种群的谱系结构。但许多现有的模型是可交换的。一个重要的挑战将是发展数学模型的不可交换的系谱结构。最后，随机概率的措施已被用作贝叶斯推理的基石。Dirichlet过程，棒断裂模型，分层Dirichlet过程，中国餐馆过程和印度自助餐过程等模型已被开发并广泛应用于统计推断。最近数据积累的爆炸性增长暴露了这些模型在捕捉真实世界现象方面的局限性。需要更复杂、更易于管理的模型。在数学上，需要开发随机测量模型，这些模型涉及强的局部和远程相互作用以及复杂的空间结构。 拟议的研究将通过开发新的随机措施和随机过程模型，设计有效的算法，以及分析各种复杂系统在不同限制制度下的渐近行为来应对这些挑战。这项研究的潜在影响远远超出了概率论。 直接应用包括但不限于生态学中的物种抽样问题、种群遗传学中的谱系结构、物理学中的自旋玻璃模型和金融工程中的投资组合理论。它将在学术和经济上为加拿大带来好处。

A random measure is a measure-valued random element. A collection of random measures becomes a measure-valued process. Random measures and measure-valued processes have been highly active research subjects over the past three decades in probability theory, stochastic processes, and statistics. Some of the well studied models include random partitions, coalescent, Dirichlet process, stick breaking model, Fleming-Viot process, and various urn models. One main motivation for their study comes from modelling complex interacting systems and the corresponding evolving dynamics. Intensive studies in this area have led to many theoretical progresses in probability theory and stochastic processes, and plenty of applications in astrophysics, chemistry, communication, ecology, economics, linguistics, machine learning, and population genetics. The progresses and advances obtained so far are nothing less than remarkable. But many challenges remain. Firstly, many random measures and processes depend on unknown parameters. Their asymptotic behaviours in various parametric regions are important features and mostly unknown. Secondly, in the study of stochastic dynamics, there is not only a strong demand in making the models closer to reality but also a clear need of calibration methods for data fitting. More efficient statistic methods need to be developed. Thirdly, mathematical models such as coalescent have been used in the study of the genealogical structure of an evolving population. But many existing models are exchangeable. An important challenge will be to develop mathematical models for non-exchangeable genealogical structures. Finally, random probability measures have been used as the building blocks of Bayesian inference. Models such as the Dirichlet process, stick breaking models, the Hierarchical Dirichlet process, Chinese restaurant process, and Indian buffet process have been developed and widely used in statistical inferences. Recent explosion in data accumulation exposes the limitations of these models in capturing real-world phenomena. More complex, manageable models are required. Mathematically one would need to develop models of random measures that involve strong local and long range interactions, and complicated spatial structures. The proposed research will address these challenges through the development of new models of random measures and random processes, the design of efficient algorithms, and the analysis of asymptotic behaviour of various complex systems under different limiting regimes. The potential impact of this research is well beyond probability theory. Direct applications include, but not limited to, the species sampling issues in ecology, the genealogical structures in population genetics, spin glass models in physics, and portfolio theory in financial engineering. It will bring benefit to Canada both academically and economically.