高斯-线性隐马尔可夫模型：一个Python软件包
The Gaussian-Linear Hidden Markov model: a Python package
AI总结：
本文提出高斯-线性隐马尔可夫模型（GLHMM）及其Python工具箱，通过线性回归参数化高斯状态分布，结合随机变分推断实现大规模神经科学数据的统计检验与样本外预测，用于发现大脑-行为关联。
摘要：
我们提出了高斯-线性隐马尔可夫模型（GLHMM），这是神经科学中常用的多种隐马尔可夫模型（HMM）类型的推广。简言之，GLHMM是一个通用框架，其中使用线性回归灵活地参数化高斯状态分布，从而适应广泛的应用场景——包括无监督、编码和解码模型。GLHMM实现为一个Python工具箱，重点强调统计检验和样本外预测——即旨在发现和刻画大脑-行为关联。该工具箱采用随机变分推断方法，使其能够在合理的计算时间内处理大规模数据集。该方法可应用于多种数据模态，包括动物记录或非大脑数据，并可应用于广泛的实验范式。作为演示，我们展示了功能磁共振成像（fMRI）、皮层脑电图（electrocorticography）、脑磁图（magnetoencephalography）和瞳孔测量（pupillometry）的示例。
英文摘要：
We propose the Gaussian-Linear Hidden Markov model (GLHMM), a generalisation of different types of HMMs commonly used in neuroscience. In short, the GLHMM is a general framework where linear regression is used to flexibly parameterise the Gaussian state distribution, thereby accommodating a wide range of uses -- including unsupervised, encoding and decoding models. GLHMM is implemented as a Python toolbox with an emphasis on statistical testing and out-of-sample prediction -- i.e. aimed at finding and characterising brain-behaviour associations. The toolbox uses a stochastic variational inference approach, enabling it to handle large data sets at reasonable computational time. The approach can be applied to several data modalities, including animal recordings or non-brain data, and applied over a broad range of experimental paradigms. For demonstration, we show examples with fMRI, electrocorticography, magnetoencephalography and pupillometry.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
