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

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis integrated with the TensorFlow ecosystem. It serves as a Bayesian deep learning framework, a probabilistic programming interface, and a variational inference engine, providing a toolset for Markov chain Monte Carlo sampling and tensor-based probabilistic modeling. The project enables the construction of neural networks with probabilistic weights and the implementation of Bayesian neural networks to quantify prediction uncertainty. It provides specialized capabilities for hierarchical probabilistic modelin

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

Doingbayesiandata_analysis is a computational framework and collection of Python scripts designed for performing Bayesian data analysis using probabilistic programming. It functions as a statistical programming port that translates analytical programs and R models from a popular statistics textbook into Python code, allowing users to execute equivalent data analyses within a modern ecosystem. The framework utilizes a probabilistic programming engine driven by Markov Chain Monte Carlo sampling backends to estimate parameter posteriors and evaluate data sets. Its execution model structures d

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

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

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

ThinkStats2 is a computational statistics course and educational library designed to teach probability and statistics through a programmatic approach. It provides a framework for studying statistical concepts by writing Python code and running simulations on real-world datasets. The project uses interactive notebooks and a collection of Python modules to deliver guided lessons. It emphasizes the verification of theoretical statistical laws through iterative computational experiments and simulation-driven testing. The resource covers broad capabilities in data analysis and data science traini

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

This repository provides a collection of Python implementations for causal inference, designed to estimate the impact of specific interventions using observational data. It serves as a statistical toolkit for researchers to isolate causal signals from complex confounding factors in data sets that lack experimental control. The framework enables the application of rigorous methodologies to study health determinants and evaluate policy interventions. By utilizing structural causal modeling and directed acyclic graphs, the library allows users to map causal dependencies and identify the necessar

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.

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

This project is a comprehensive, crowdsourced directory of mathematical resources, functioning as a decentralized index of external educational materials. It organizes a vast collection of textbooks, lecture notes, and research tools into a hierarchical taxonomy, allowing users to navigate diverse mathematical disciplines through a version-controlled repository. The collection distinguishes itself by acting as a central hub for both academic discovery and practical application. It provides access to a wide array of interactive software, visualization tools, and step-by-step solvers designed t

This repository serves as a technical reference and educational resource for implementing software design patterns within the Swift programming language. It provides a collection of common architectural patterns designed to help developers structure codebases for improved maintainability, scalability, and system organization. The project focuses on applying fundamental object-oriented and protocol-oriented principles to manage relationships between classes and objects. It demonstrates how to use language-level interfaces to define shared behaviors and how to organize components into cohesive

All about erlang programming language powerd by community

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 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 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

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

This project is a collection of Bayesian statistics courseware and educational resources. It provides instructional materials, problem sets, and solutions designed for learning Bayesian data analysis and causal modeling. The repository includes a suite of statistical data visualization scripts used to generate instructional animations and plots. It also contains code examples that implement Bayesian modeling and survival analysis across multiple programming languages to demonstrate different computational approaches. The materials cover a range of statistical capabilities, including causal 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