IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xi Zhang, Akshay Aravamudan, Georgios C Anagnostopoulos
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引用次数: 0

摘要

时间点过程对于神经科学和社交媒体等领域的事件动态建模至关重要。时间重定标定理通常通过将点过程转换为同质泊松过程来评估模型拟合度。然而,这种方法要求过程是非终结的,并且能观察到完整的(因此是无界的)实现--这些条件在实践中往往无法满足。本文引入了一个广义的时间缩放定理来解决这些局限性,从而为点过程模型在现实世界的各种场景中提供了一个更广泛适用的评估框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Generalized Time Rescaling Theorem for Temporal Point Processes.

Temporal point processes are essential for modeling event dynamics in fields such as neuroscience and social media. The time rescaling theorem is commonly used to assess model fit by transforming a point process into a homogeneous Poisson process. However, this approach requires that the process be nonterminating and that complete (hence, unbounded) realizations are observed-conditions that are often unmet in practice. This article introduces a generalized time-rescaling theorem to address these limitations and, as such, facilitates a more widely applicable evaluation framework for point process models in diverse real-world scenarios.

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来源期刊
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
审稿时长
3.0 months
期刊介绍: Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.
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