2 个仓库
Awesome GitHub RepositoriesStatistical Pattern Detection
Methods for determining if data follows specific statistical distributions or trends.
Distinct from Data Detection Patterns: Candidates focus on regex for strings or DOM patterns rather than statistical data distributions.
Explore 2 awesome GitHub repositories matching data & databases · Statistical Pattern Detection. Refine with filters or upvote what's useful.
Awesome Statistical Pattern Detection GitHub Repositories
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- hosseinmoein/dataframe
hosseinmoein/DataFrame
2,917在 GitHub 上查看DataFrame is a C++ tabular data library and manipulation engine designed for managing heterogeneous data in contiguous memory. It functions as a statistical analysis framework and time series analysis toolkit, providing the means to store, index, and transform multidimensional datasets. The project distinguishes itself through a high-performance execution model that utilizes column-major storage, SIMD-aligned memory allocation, and a thread-pool for parallel computations. It employs a visitor-based algorithm dispatch system and policy-driven transformations to decouple data processing logic f
Determines if column values follow specific statistical distributions or monotonicity trends.
C++aicppdata-analysis - krasserm/bayesian-machine-learning
krasserm/bayesian-machine-learning
1,916在 GitHub 上查看该项目是一个教育性的计算笔记本和教程集合，专注于贝叶斯机器学习和概率编程。它提供了一个构建预测模型的框架，通过定义参数的概率分布而不是依赖单一的点估计来表示不确定性。 该仓库作为一个统计方法库，用于估计参数分布、执行回归以及量化预测系统中的置信水平。它涵盖了一系列技术，包括高斯过程回归、马尔可夫链蒙特卡洛（MCMC）采样和变分推理，以近似复杂的后验分布。 除了核心回归和推理外，该集合还演示了如何识别高维数据集中的潜在结构，并通过概率代理建模自动化搜索最佳模型配置。这些资源被组织为分步教程，旨在促进概率模型和不确定性量化技术的实际应用。
Identifies underlying structures in complex datasets by applying generative models to simplify high-dimensional information into core features.
Jupyter Notebookbayesian-machine-learningbayesian-methodsbayesian-optimization
