Search Results for "probability"
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Showing 223 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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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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pomegranate
Fast, flexible and easy to use probabilistic modelling in Python
pomegranate is a library for probabilistic modeling defined by its modular implementation and treatment of all models as the probability distributions they are. The modular implementation allows one to easily drop normal distributions into a mixture model to create a Gaussian mixture model just as easily as dropping a gamma and a Poisson distribution into a mixture model to create a heterogeneous mixture. But that's not all! Because each model is treated as a probability distribution, Bayesian networks can be dropped into a mixture just as easily as a normal distribution, and hidden Markov models can be dropped into Bayes classifiers to make a classifier over sequences. ... -
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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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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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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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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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ProbabilisticCircuits.jl
Probabilistic Circuits from the Juice library
...Probabilistic Circuits provides a unifying framework for several family of tractable probabilistic models. PCs are represented as computational graphs that define a joint probability distribution as recursive mixtures (sum units) and factorizations (product units) of simpler distributions (input units). Given certain structural properties, PCs enable different range of tractable exact probabilistic queries such as computing marginals, conditionals, maximum a posteriori (MAP), and more advanced probabilistic queries. -
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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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PyMC3
Probabilistic programming in Python
...Sometimes an unknown parameter or variable in a model is not a scalar value or a fixed-length vector, but a function. A Gaussian process (GP) can be used as a prior probability distribution whose support is over the space of continuous functions. PyMC3 provides rich support for defining and using GPs. Variational inference saves computational cost by turning a problem of integration into one of optimization. PyMC3's variational API supports a number of cutting edge algorithms, as well as minibatch for scaling to large datasets. -
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Hasktorch
Tensors and neural networks in Haskell
Hasktorch is a powerful Haskell library for tensor computation and neural network modeling, built on top of libtorch (the backend of PyTorch). It brings differentiable programming, automatic differentiation, and efficient tensor operations into Haskell’s strongly typed functional paradigm. This project is in active development, so expect changes to the library API as it evolves. We would like to invite new users to join our Hasktorch discord space for questions and discussions.... -
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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. -
15
wego
Weather app for the terminal
...You can set the $WEGORC environment variable to override the default config file location. Displayed info (metric or imperial units), temperature range (felt and measured), windspeed and direction, viewing distance, precipitation amount and probability. -
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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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Deep-Learning-Interview-Book
Interview guide for machine learning, mathematics, and deep learning
Deep-Learning-Interview-Book collects structured notes, Q&A, and concept summaries tailored to deep-learning interviews, turning scattered study into a coherent playbook. It spans the core math (linear algebra, probability, optimization) and the practitioner topics candidates actually face, like CNNs, RNNs/Transformers, attention, regularization, and training tricks. Explanations emphasize intuition first, then key formulas and common pitfalls, so you can reason through unseen questions rather than memorize trivia. Many entries connect theory to implementation details, including how choices in activation, initialization, or normalization affect convergence and stability. ... -
18
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. ... -
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Gonum
Set of numeric libraries for the Go programming language
...Gonum is a set of packages designed to make writing numerical and scientific algorithms productive, performant, and scalable. Gonum contains libraries for matrices and linear algebra; statistics, probability distributions, and sampling; tools for function differentiation, integration, and optimization; network creation and analysis; and more. We encourage you to get started with Go and Gonum if you are tired of sluggish performance, and fighting C and vectorization, and also if you are struggling with managing programs as they grow larger. ... -
20
Bayesian Julia
Bayesian Statistics using Julia and Turing
...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. Bayesian statistics incorporate uncertainty (and prior knowledge) by allowing probability statements about parameters. -
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ramsey/uuid
A PHP library for generating universally unique identifiers (UUIDs)
...A universally unique identifier, or UUID, is a 128-bit unsigned integer, usually represented as a hexadecimal string split into five groups with dashes. The most widely-known and used types of UUIDs are defined by RFC 4122. The probability of duplicating a UUID is close to zero, so they are a great choice for generating unique identifiers in distributed systems. UUIDs can also be stored in binary format, as a string of 16 bytes. The JSON extension is normally enabled by default, but it is possible to disable it. Other required extensions include PCRE and SPL. These standard extensions cannot be disabled without patching PHP’s build system and/or C sources. -
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Lung cancer screening panel
Software for predicting probability of lung cancer from VCF file
We have developed a lung cancer screening panel based on the occurrence of specific clonal hematopoietic mutations in peripheral blood samples. The software provided here can be used to predict the probability of cancer from peripheral blood samples. The sequencing data from the blood sample should be first aligned to the GRCh38 reference genome using the Burroughs-Wheeler aligner and variants called using the Mutect2 for variant calling. The resulting VCF file is used as input to the software. Output: Predicted probability of cancer. -
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MinSufPref
P-value of patterns occurrences
Given a pattern H, i.e. a set of words in a given alphabet A. Let T be a random text of size n generated according to a given probability model. Current version of the program supports Bernoulli model only. The program computes the probability to find at least s (possibly overlapping) occurrences of words from a pattern H in a random text T. -
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ls-recession-indicator
Unemployment rate-based Least Squares Recession Indicator
This project attempts to create a machine learning model which predicts the probability of recession for any given month based on the current month plus the 11 prior months of U.S. U-3 unemployment rate data. It utilizes a linear regression / least squares algorithm. -
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Mathpaqs
A collection of mathematical packages in pure Ada
Various mathematical packages including algebra, finite elements, random variables, probability dependency models, unlimited integers. Pure Ada, fully portable. More information on... http://mathpaqs.sf.net Alire crate: https://alire.ada.dev/crates/mathpaqs Mirror: https://github.com/zertovitch/mathpaqs
