Search Results for "probability"
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Showing 203 open source projects for "probability"
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Collaborate is customizable case management software for non-profits and social services agencies with teams of 5+ staff.
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BackBox seamlessly integrates with network monitoring and NetOps platforms and automates configuration backups, restores, and change detection. BackBox also provides before and after config diffs for change management, and automated remediation of discovered network security issues.
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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. Since TFP inherits the benefits of TensorFlow, you can build, fit, and deploy a model... -
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Pyro
Deep universal probabilistic programming with Python and PyTorch
Pyro is a flexible, universal probabilistic programming language (PPL) built on 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... -
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Distributions.jl
A Julia package for probability distributions and associated functions
A Julia package for probability distributions and associated functions. -
Your applications are all built differently, but they all need to perform. NeoLoad simplifies and scales performance testing for everything, from APIs and microservices, to end-to-end application testing through innovative protocol and browser-based capabilities.
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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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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... -
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Bayesian Statistics
This repository holds slides and code for a full Bayesian statistics
... 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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MeasureTheory.jl
"Distributions" that might not add to one.
Probabilistic programming and statistical computing are vibrant areas in the development of the Julia programming language, but the underlying infrastructure dramatically predates recent developments. The goal of MeasureTheory.jl is to provide Julia with the right vocabulary and tools for these tasks. In this package we introduce well-chosen foundational primitives centered around the notion of measure, density and conditional probability with powerful combinators and transforms intended... -
AlertBot monitors your website's full functionality around the clock so you can focus your time on more important things.
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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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ProbabilisticCircuits.jl
Probabilistic Circuits from the Juice library
This module provides a Julia implementation of Probabilistic Circuits (PCs), tools to learn structure and parameters of PCs from data, and tools to do tractable exact inference with them. 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... -
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Bayesian Julia
Bayesian Statistics using Julia and Turing
... 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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CausalNex
A Python library that helps data scientists to infer causation
CausalNex is a Python library that uses Bayesian Networks to combine machine learning and domain expertise for causal reasoning. You can use CausalNex to uncover structural relationships in your data, learn complex distributions, and observe the effect of potential interventions. -
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NeuroMatch Academy (NMA)
NMA Computational Neuroscience course
... brush up on your knowledge. The pre-reqs refresher days are asynchronous, so you can go through the material on your own time. 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
... 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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Gonum
Set of numeric libraries for the Go programming language
Gonum is a set of numeric libraries for the Go programming language. It contains libraries for matrices, statistics, optimization, and more. 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... -
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PyMC3
Probabilistic programming in Python
... 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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wego
Weather app for the terminal
... 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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ramsey/uuid
A PHP library for generating universally unique identifiers (UUIDs)
... 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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A tool that can be used to make custom discrete probability distributions using bezier curves
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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... -
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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 -
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eprb_signal_correlations
Simulation of a two-channel Bell test, with closed-form proofs
Derivation, entirely by probability theory, of the correlation coefficient for a two-channel Bell test, with simulation in Ada and other languages. The Nobel Committe for Physics bans this program for subversive content. (Mirror of the repository at https://github.com/chemoelectric/eprb_signal_correlations) -
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Statistics101 - Resampling Statistics
Use simulation to perform statistical analyses.
Statistics101 is an Integrated Development Environment (IDE) that uses a simple, powerful language called “Resampling Stats” to develop Monte Carlo programs to analyze and solve statistical problems. The original Resampling Stats language and computer program were developed by Dr. Julian Simon and Peter Bruce as a new way to teach Statistics to social science students. Of course, social science students aren't the only ones who can benefit. Anyone who wants to learn statistics will find that... -
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LibRan
Random Variate generators for various distributions.
The LibRan package is a library of various pseudo-random number generators along with their exact probability and cumulative probability density functions. The libary contains its own optimized sequential congruential uniform pseudo-random number generator on the interval x ∈ [0, 1) ; along with useful tools such as methods for collecting statistics in bins. Each of the random variate distributions rely on a number of internal attributes to customize the distribution. The library is written...