NoMoPy is a code for fitting, analyzing, and generating noise modeled as a hidden Markov model (HMM) or, more generally, factorial hidden Markov model (FHMM). This code, written in Python, implements approximate and exact expectation maximization (EM) algorithms for performing the parameter estimation process, model selection procedures via cross-validation, and parameter confidence region estimation. Here, we describe in detail the functionality implemented in NoMoPy and provide examples of its use and performance on example problems.

中文翻译：

---

NoMoPy：Python 中的噪声建模

NoMoPy 是一种用于拟合、分析和生成噪声的代码，该噪声建模为隐马尔可夫模型 (HMM)，或更一般地说，阶乘隐马尔可夫模型 (FHMM)。该代码用 Python 编写，实现了近似和精确期望最大化 (EM) 算法，用于执行参数估计过程、通过交叉验证的模型选择过程以及参数置信区域估计。在这里，我们详细描述了 NoMoPy 中实现的功能，并提供了其在示例问题上的使用和性能示例。