Search Results for "probability"
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Showing 192 open source projects for "probability"
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TensorFlow Probability
Probabilistic reasoning and statistical analysis in TensorFlow
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. -
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Book5_Essentials-Probability-Statistics
The book 5 of statistics in simplicity
...The material connects probability theory directly to real analytical workflows, helping learners understand how statistics supports predictive modeling. Like the other books in the series, it blends mathematical explanation with Python-based experimentation. Overall, the project provides a practical statistical foundation for students advancing into AI and data science. -
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Distributions.jl
A Julia package for probability distributions and associated functions
A Julia package for probability distributions and associated functions. -
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GPflow
Gaussian processes in TensorFlow
GPflow is a package for building Gaussian process models in Python. It implements modern Gaussian process inference for composable kernels and likelihoods. GPflow builds on TensorFlow 2.4+ and TensorFlow Probability for running computations, which allows fast execution on GPUs. -
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AbstractGPs.jl
Abstract types and methods for Gaussian Processes
AbstractGPs.jl is a package that defines a low-level API for working with Gaussian processes (GPs), and basic functionality for working with them in the simplest cases. As such it is aimed more at developers and researchers who are interested in using it as a building block than end-users of GPs. You may want to go through the main API design documentation. -
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MathPHP
Powerful modern math library for PHP
Math PHP is a library that brings advanced mathematical functions and data analysis capabilities to PHP applications. It covers a wide range of topics, including linear algebra, calculus, statistics, probability, and numerical analysis. Math PHP is designed for developers and data scientists who require precise and efficient mathematical computations in PHP, making it suitable for scientific computing and data processing. -
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Laya-MLX
Native MLX runtime for Laya typed decision models
Laya-MLX is an independent MLX implementation of Laya’s typed decision models for Apple Silicon Macs. It performs structured choice, score, and probability decisions without token-by-token text generation. Inference runs fully locally after model weights are downloaded and does not require PyTorch, Transformers, or a cloud API. The runtime supports English, multilingual, and typed-decision Laya checkpoints while preserving their original calibration and output formats. Its implementation moves the encoder, decision transformer, scoring head, and action head into MLX. ... -
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Kalshi Trading Bot CLI
AI-native CLI for trading Kalshi prediction markets
Kalshi Trading Bot CLI is an AI-driven command-line tool designed to automate trading strategies on Kalshi prediction markets by combining quantitative modeling with real-time market data. It operates by conducting deep research on events, generating independent probability estimates, and comparing those estimates against current market prices to identify trading opportunities. The system incorporates advanced decision-making logic, including Kelly criterion-based position sizing and a structured multi-step risk evaluation process before executing trades. Built as a CLI application, it allows traders to interact programmatically with markets, making it suitable for automation and integration into larger trading pipelines. ... -
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Nano ID
A secure, URL-friendly, unique string ID generator for JavaScript
Nano ID is a library for generating random IDs. Likewise UUID, there is a probability of duplicate IDs. However, this probability is extremely small. Meanwhile, a lot of projects generate IDs in small numbers. For those projects, the ID length could be reduced without risk. This calculator aims to help you realize the extent to which the ID length can be reduced. Instead of using the unsafe Math.random(), Nano ID uses the crypto module in Node.js and the Web Crypto API in browsers. ... -
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Kev
Jev-like family of decision models built on top of Qwen3.5/3.8
...Supported question formats include yes-or-no, multiple choice, and ordered scoring. Checkpoints range from a compact 0.8B model for smaller hardware to a 27B version for high-end systems. The models include probability calibration and can run through CUDA, ROCm, or MLX depending on hardware. Kev exposes an API compatible with TypeSafe System One, allowing compatible clients to target a locally hosted server. Developers can also fine-tune models on their own labeled examples and deploy private HTTPS endpoints. -
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SemIf
Semantic ifs from open models, on a 3090 at home
SemIf, formerly OpenJev, is an independent research project for making small semantic decisions with open language models. Instead of generating text and parsing it afterward, it reads probabilities directly from declared answer options. Each request supplies state, a runtime-defined question, and typed choices, allowing the model to behave like a semantic conditional. Shared-state execution can reuse a long context across many related decisions to reduce repeated computation. Backends... -
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Bayesian Statistics
This repository holds slides and code for a full Bayesian statistics
...The posterior can also be used for making predictions about future events. Bayesian statistics is a departure from classical inferential statistics that prohibits probability statements about parameters and is based on asymptotically sampling infinite samples from a theoretical population and finding parameter values that maximize the likelihood function. Mostly notorious is null-hypothesis significance testing (NHST) based on p-values. -
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Mathematics Dataset
This dataset code generates mathematical question and answer pairs
...It generates question-and-answer pairs across a wide range of mathematical topics typically found in school-level curricula, testing a model’s ability to reason about algebra, arithmetic, calculus, probability, and more. Each question is programmatically generated with structured templates to ensure clear logic and reproducibility. The dataset enables models to learn mathematical problem-solving through examples that involve both numeric and symbolic reasoning. Version 1.0 includes over 2 million examples per category, with training splits labeled as “easy,” “medium,” and “hard,” supporting curriculum-based learning strategies. ... -
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NanoJev
A nano replica of Jev: parallel decisions, dynamic candidates
NanoJev is an open-source 0.6B-parameter decision model created as a compact reproduction of Jev-style parallel decision making. It takes states and questions as input and returns complete probability distributions without decoding output tokens one by one. The project supports dynamic candidate sets so the available actions can change from one decision to the next. One shared checkpoint has been tested across ViZDoom aiming tasks, maze navigation, and Snake. The repository includes datasets, configurations, research results, demonstrations, and an end-to-end training pipeline. ... -
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ThinkStats2
Text and supporting code for Think Stats, 2nd Edition
ThinkStats2 is the code and text companion for the second edition of Think Stats, an introduction to statistics and data science for Python programmers. It teaches probability and statistical reasoning through short programs, experiments, and analysis of real datasets. The material emphasizes exploratory methods that help readers ask and answer practical questions with data. Case studies draw from public sources, including health-related datasets, to connect abstract concepts with realistic analysis. ... -
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Think Bayes
Code repository for Think Bayes
...Instead of heavy focus on continuous mathematics or calculus, the book emphasizes learning Bayesian inference by writing Python programs. The project includes code examples, scripts, and environments that correspond to the chapters of the book. Learners can run the code, experiment with probability distributions, compute posterior probabilities, and understand Bayesian updating via simulation and algorithmic methods. The book and code encourage thinking in terms of discrete approximations (sums over distributions) rather than continuous integrals, making it more accessible to many programmers. Over time, the repository has been updated (including a second edition version) to reflect improved practices, corrections, and modern Python tooling. -
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AiLearning-Theory-Applying
Quickly get started with AI theory and practical applications
...It includes well-commented notebooks, datasets, and implementation examples that allow learners to reproduce experiments and understand the inner workings of various algorithms. The project also introduces important concepts such as probability theory, linear algebra, regression models, clustering methods, and neural network architectures. Advanced sections explore modern AI topics including transformers, BERT-based natural language processing systems, and practical competition-style machine learning workflows. -
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Pyro
Deep universal probabilistic programming with Python and PyTorch
...It allows for expressive deep probabilistic modeling, combining the best of modern deep learning and Bayesian modeling. Pyro is centered on four main principles: Universal, Scalable, Minimal and Flexible. Pyro is universal in that it can represent any computable probability distribution. It scales easily to large datasets with minimal overhead, and has a small yet powerful core of composable abstractions that make it both agile and maintainable. Lastly, Pyro gives you the flexibility of automation when you want it, and control when you need it. -
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Jev Ultrafast
A browser agent with a dynamic, indexed action space
Jev Ultrafast is an experimental browser agent built around a dynamic, indexed action space for fast web interaction. Each browser state is converted into a numbered table of currently available interactive elements. A policy chooses both an operation and a compatible target in a single network round trip. Supported actions include clicking, typing, selecting, scrolling, waiting, finishing, and reporting that a task is blocked. A small language model is invoked only when text must actually... -
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pytudes
Python programs, usually short, of considerable difficulty
...The name is inspired by musical études, meaning compact exercises designed to build mastery through focused practice. The repository includes readable solutions, experiments, notebooks, and scripts that cover algorithms, puzzles, probability, search, language processing, simulation, and mathematical reasoning. It is useful for programmers who want to study elegant Python code while learning how experienced developers approach problem solving. Many examples emphasize clarity and compactness rather than framework-heavy engineering. pytudes is best understood as a learning library, a coding style reference, and a set of practical programming studies. -
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NeuroMatch Academy (NMA)
NMA Computational Neuroscience course
...You will learn how to code in Python from scratch using a simple neural model, the leaky integrate-and-fire model, as a motivation. Then, you will cover linear algebra, calculus and probability & statistics. The topics covered on these days were carefully chosen based on what you need for the comp neuro course. -
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RuoYi
The warehouse's SpringBoot-based rights management system
...She can be used for all web applications, such as website management background, website member center, CMS, CRM, OA. All front-end and back-end codes are very compact and easy to use after encapsulation, and the probability of errors is low. Also supports mobile client access. The system will continue to update some useful functions. The user is the system operator, and this function mainly completes the system user configuration. Configure the system organization (company, department, group). -
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Laya
Non-autoregressive System 1 decision engine
Laya is an open-source, non-autoregressive decision engine designed to answer structured questions without generating free-form text. It supports choice, score, and yes-or-no probability decisions in a single forward pass. The system works across more than 100 languages and uses a router to select an appropriate checkpoint for each request. It can run locally on CPU or GPU hardware and includes Python and TypeScript tooling. Integrations are available for HTTP, MCP, LangChain, LangGraph, LlamaIndex, CrewAI, and ONNX workflows. ... -
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NBA Sports Betting Machine Learning
NBA sports betting using machine learning
...Using this dataset, the project constructs matchup features that represent team performance trends and contextual information about each game. Machine learning models are then trained to estimate the probability that a team will win a game as well as whether the total score will fall above or below the sportsbook’s predicted total. In addition to predicting outcomes, the project evaluates expected value to determine whether a potential bet offers a statistical advantage compared with sportsbook odds. -
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latexify
A library to generate LaTeX expression from Python code
...It parses Python functions and expressions into an abstract syntax tree (AST), applies symbolic rewrites for common mathematical constructs, and then emits LaTeX that compiles cleanly in standard environments. Typical use cases include turning analytical utilities—like probability mass functions, activation formulas, or recurrence relations—into equations suitable for papers, notebooks, and slide decks. The tool aims to preserve semantics such as exponentiation, summations, products, piecewise definitions, and function application while hiding Pythonic scaffolding. Users can control rendering details for names and operators so the output conforms to a project’s notation style. ...
