Open-source alternatives to Probabilistic Programming And Bayesian Methods For Hackers

Edward is a probabilistic programming language and inference engine designed for building deep generative models and Bayesian neural networks. It utilizes the TensorFlow framework to represent probabilistic models as differentiable computational graphs. The library enables the construction of complex data distributions through Bayesian neural networks, mixture models, and Gaussian processes. It differentiates itself by providing an integrated toolkit for both supervised and unsupervised probabilistic modeling, including the implementation of generative adversarial networks and mixture density

Pyro is a probabilistic programming language and library built for PyTorch. It serves as a Bayesian inference engine and a tool for probabilistic graphical modeling, allowing users to define generative models that combine neural networks with probabilistic logic. The framework enables deep probabilistic programming by integrating probability distributions into computational graphs. This allows for the quantification of uncertainty in deep learning models and the execution of scalable posterior distribution calculations for complex data dependencies. The system provides a suite of inference c

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

Lihang is a statistical learning algorithm library and framework providing implementations of supervised and unsupervised machine learning models. It functions as a reference repository that translates statistical learning theories into executable code for data classification and pattern recognition. The project features specialized tools for probabilistic model implementation, utilizing likelihood estimation and Bayesian methods to determine optimal model parameters. It includes a sequential data labeling tool for identifying patterns in ordered data sequences and supports both linear and no

This repository serves as an educational resource for Bayesian statistical modeling, providing a collection of instructional examples that translate theoretical concepts into executable Python code. It functions as a computational framework for performing statistical inference and parameter estimation, designed to help users learn and apply probabilistic programming techniques through interactive documentation. The project utilizes a probabilistic programming framework to define statistical models as directed acyclic graphs, enabling automated inference through advanced sampling algorithms. B

This library is a collection of machine learning algorithms and neural network components implemented from scratch using only NumPy. It serves as an educational toolkit for constructing and experimenting with machine learning architectures, emphasizing a modular approach where algorithms are organized into self-contained, object-oriented classes. The project distinguishes itself by relying exclusively on array-oriented programming to perform mathematical operations, ensuring that all computations are vectorized for performance. By utilizing a standardized interface for forward and backward pa

This project is a machine learning study guide and technical knowledge base. It serves as a version-controlled repository of mathematical formulas and algorithmic explanations, providing instructional material and reference notes for the study of artificial intelligence. The content is structured as a markdown-based knowledge base that pairs theoretical mathematical explanations directly with code implementations. This approach demonstrates model mechanics in practice across several specialized domains, including deep learning research, probabilistic graphical modeling, and reinforcement lear

This project is an educational resource and toolkit for implementing Bayesian estimation and Kalman filters in Python. It provides a framework for constructing linear and non-linear filters to estimate the state of dynamic systems by combining noisy sensor data with mathematical process models. The library focuses on probabilistic state estimation, utilizing recursive Bayesian updating and state-space mathematical modeling to refine beliefs about system states. It includes utilities for simulating dynamic systems, allowing users to generate synthetic trajectories and sensor observations to va

QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial time-series analysis, and systematic trading. It provides an event-driven backtesting environment for validating strategies against historical tick and bar data, alongside a dedicated portfolio optimization engine for calculating asset weights and risk metrics. The project distinguishes itself through a machine learning finance toolkit that implements recurrent neural networks for price prediction and reinforcement learning for derivative pricing. It also features advanced statisti

This project is a machine learning implementation library featuring a collection of code examples that implement supervised, unsupervised, and reinforcement learning algorithms from scratch. It provides a comprehensive set of toolkits for core machine learning components, including a natural language processing toolkit, a reinforcement learning framework, and suites for data dimensionality reduction and pattern mining. The library includes specialized implementations for reinforcement learning, such as Q-Learning, Deep Q-Networks, and Actor-Critic agents. The natural language processing capab

This project is an educational resource focused on machine learning mathematics education. It provides a curriculum for the mathematical foundations required to understand and implement machine learning algorithms, covering linear algebra, calculus, probability, and optimization. The resource includes structured mathematics modules and a foundation curriculum paired with practice exercises, instructor manuals, and solution guides. It offers technical textbook supplementation through downloadable PDF materials and supplementary learning content such as video lectures and presentation slides.

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and…

Probabilistic programming powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping. The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st

Kronos is a financial time-series forecasting framework and quantitative trading strategy simulator. It functions as a research environment designed to analyze historical market data, train predictive models, and evaluate the performance of automated trading signals. The platform distinguishes itself through its deep learning sequence predictors and probabilistic market modeling tools. By utilizing sequence-based architectures and statistical sampling, the system generates multiple potential price trajectories and volatility estimates to quantify uncertainty. It also supports transfer learnin

This is a generative AI model library containing a collection of PyTorch and TensorFlow implementations for creating synthetic data and modeling complex probability distributions. It serves as a multi-framework repository of deep learning models designed for learning and replicating data patterns. The project provides specialized implementation suites for several generative architectures. This includes Generative Adversarial Networks using competing generator and discriminator models, Variational Autoencoder frameworks that map data to a latent space, and Restricted Boltzmann Machine and Deep

Guess is a predictive page loading library that uses machine learning to prefetch JavaScript bundles and assets. It functions as a resource prefetcher that predicts the next page a user will visit by utilizing a web application route parser and a user behavior analytics integrator. The project distinguishes itself by importing navigation patterns from analytics APIs to inform its predictive models. It uses probabilistic navigation modeling and historical transition data to calculate the likelihood of future page visits, allowing for the proactive download of lazy-loaded bundles. The system i

GluonTS is a probabilistic time series library and deep learning forecasting framework. It provides a toolkit for building, training, and evaluating neural network architectures that predict future values as probability distributions to quantify uncertainty. The project distinguishes itself by supporting zero-shot forecasting and integrating diverse modeling approaches, including deep probabilistic neural networks and wrappers for external statistical libraries such as Prophet and R forecast. It implements specialized architectural primitives like causal convolutions and invertible residual n

GluonTS is a framework for probabilistic time series forecasting, designed to predict future values as probability distributions with confidence intervals. It supports both traditional model training and zero-shot forecasting, where pretrained models generate predictions for new series without additional training. The project distinguishes itself by integrating a wide variety of forecasting approaches into a unified workflow. This includes deep learning architectures such as recurrent neural networks and causal convolutions, as well as the integration of external statistical models, the Proph

This repository contains the digital textbook and supplementary materials for probabilistic machine learning education. It provides structured text and guided study materials covering the mathematical foundations of probability and neural networks. The project emphasizes reproducibility through a collection of interactive notebooks and standalone scripts used to recreate data plots and figures from the text. These materials are hosted in external environments to allow users to execute complex machine learning code without local installation. The educational surface includes lecture slides, e

This project is an algorithm courseware repository and academic resource portal. It serves as a digital archive for algorithm textbooks, providing access to complete manuscripts, individual chapters, and educational materials focused on computer science fundamentals and algorithm design. The repository includes a dedicated errata tracking system to record publication errors and corrections. This system allows for the monitoring of updates made to the academic texts since their official release to ensure the accuracy of the information. The platform distributes a variety of supplemental cours

This project is a digital collection of academic material on deep learning provided as a machine learning educational resource. It delivers the complete textbook and individual chapters in portable document format for offline study and research. The repository includes electronic publication versions of the textbooks optimized for digital reading devices and e-book readers. It functions as a segmented document repository, providing the text both as a full volume and split into individual chapters to allow for targeted reading.

Prophet is a predictive analytics framework and time series regression library designed for forecasting future values. It uses additive models to fit non-linear growth and periodic seasonal patterns, providing tools for producing forecasts with integrated error measurement. The project handles multiple seasonalities and holiday effects to improve accuracy for periodic data. It supports the integration of external regressors and manages data irregularities, such as missing data and outliers, to maintain prediction stability. The framework covers a broad range of analysis capabilities, includi

Riskfolio-Lib is a Python portfolio optimization library and convex risk management tool. It provides a framework for calculating optimal asset allocations using convex risk measures and mathematical programming solvers, supporting linear, quadratic, and semidefinite programming. The library features a hierarchical risk parity framework and financial asset clustering tools to group similar instruments and improve diversification. It includes a portfolio backtesting engine for simulating investment strategies using historical data and cross-validation. The system covers a broad range of quant

Statsmodels is a comprehensive Python library designed for statistical modeling, econometric research, and data analysis. It provides a robust framework for estimating and diagnosing a wide range of statistical models, enabling users to perform rigorous hypothesis testing, regression analysis, and complex data exploration within structured environments. The library distinguishes itself through its support for advanced statistical methodologies, including state space representation for dynamic systems and generalized linear frameworks that accommodate non-normal response variables. It offers s

This project is a community-driven repository of high-quality, university-level computer science courses and learning materials. It serves as an open-source knowledge base, providing developers and students with direct access to structured curricula and academic resources designed to facilitate independent study and technical skill development. The repository distinguishes itself through a hierarchical taxonomy that organizes diverse technical subjects into a navigable structure. By utilizing markdown-based content curation, the project maintains a lightweight index of external links and refe

MLAlgorithms is an educational machine learning algorithm library consisting of core predictive models implemented from scratch in Python. It serves as a reference for developers to study the internal logic and mathematical workings of these models through clean, minimal implementations. The codebase focuses on the study of algorithm implementation and machine learning education, providing a way to understand internal mechanics by building components without relying on heavy external libraries. The project utilizes object-oriented encapsulation and NumPy-based vectorization to manage model s