Displaying 1 to 20 from 30 results

stat-cookbook - :orange_book: The probability and statistics cookbook

•    TeX

The probability and statistics cookbook contains a succinct representation of various topics in probability theory and statistics. It provides a comprehensive mathematical reference reduced to its essence, rather than aiming for elaborate explanations. Feel encouraged to extend the cookbook by forking it and submitting pull requests.

simple-statistics - simple statistics for node & browser javascript

•    Javascript

A JavaScript implementation of descriptive, regression, and inference statistics. Implemented in literate JavaScript with no dependencies, designed to work in all modern browsers (including IE) as well as in node.js.

Algorithm - Algorithm is a library of tools that is used to create intelligent applications.

•    Swift

Algorithm is a library of tools that is used to create intelligent applications. Embedded frameworks require a minimum deployment target of iOS 8 or OS X Mavericks (10.9).

aftermath

•

Provides sse/avx implementation for matrix storage, access and basic operations, probability distributions and fast ziggurat random number generators.

Media Assistant

•

Those who loves music spend lot of time to select music to play. What if someone who understands your flavor of music and plays according to that. WOW! What a great life. Media assistant learn from you to understand your choice of music and plays music for you.

Python-for-Probability-Statistics-and-Machine-Learning - Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

•    Jupyter

Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

chi-squared.js - characteristic functions for the chi-squared distribution

•    Javascript

Characteristic functions for the chi-squared distribution.Compute the probability density function for the parameter x under k degrees of freedom.

gtest.js - g-test algorithm to compute statistical significance

•    Javascript

Calculate a G-test.Return the liklihood ratio from the populations a and b.

Probability

•    Javascript

Probability.js makes it easy to call JavaScript functions by probability in Node.js and the browser. Circumstances are rare that you need to call functions by a certain probability in your daily work. But sometimes, especially in game development and in statistical applications, it's very handy to have an easy way of doing so. This library is inspired by this question on stackoverflow.com.

•    Javascript

mctad.js is a JavaScript library for probability and statistics. A goal is to provide functionality missing from other such libraries, including the ability to generate random variables from many common discrete and continuous probability distributions, and having a basic implementation of directional statistics. You'll want either the file mctad.min.js or the file mctad.js in the root of this repository.

machine-translator - Translate words using a statistical model

•    Javascript

is a nodejs module that uses statistical machine translation to translate between two different languages. the module is loosely based off of the IBM model 1 algorithm and has been tested using english.

ChiSquare - Calculates a Chi-square distribution over a sequence of bytes within a Buffer

•    Javascript

Calculates a Chi-square distribution over a sequence of bytes within a Buffer. The result is a float representing the probability of how frequently a truly random sequence of bytes would exceed the calculated value.

estimates-template - Write user stories in markdown, list the implementation tasks for each (with an estimation range), publish an interactive estimation document

•    Javascript

Write user stories in Markdown, list the implementation tasks for each (with an estimation range), publish an interactive estimation document, like that one. Edit the header and footer HTML in the ./index.js.

DNest4 - Diffusive Nested Sampling

•    C++

DNest4 is a C++11 implementation of Diffusive Nested Sampling, a Markov Chain Monte Carlo (MCMC) algorithm for Bayesian Inference and Statistical Mechanics. There is a manuscript describing DNest4 installation and usage in the paper/ directory of this repository. You can compile it with pdflatex. Alternatively, you can get the preprint here.

statsjs - Provides functions for many of the statistical operations that you might need

•    Javascript

Provides functions for many of the statistical operations that you might need. It also supports many of the functions on the data set that you'd expect from Underscore, such as pluck, map, and each.

js-weighted-list - A small javascript library implemented a weighted-probability list

•    Javascript

This is a smallish library which implements a weighted list, from which elements can be picked out at random with a probability dependent on their weight. The list implements random sampling without replacement. This project initially grew from a javascript reimplementation of this Stack Overflow answer by Jason Orendorff, and it still uses basically the same algorithm.

prob.js - Generate random numbers from different probability distributions.

•    Javascript

Generate random numbers from different probability distributions. Demo. Internally Prob.js uses Mersenne Twister provided by random-js. This can be overridden by providing the src argument when generating a number. src is expected to be a function that when called returns a signed integer uniformly in the range [-2^31,2^31).

randomsys - Algorithmic study of random systems

•    Jupyter

We study the behavior of random systems algorithmically. This notebook gives a DEMONSTRATION of the useful functions which yield true random numbers produced live from an experiment in quantum mechanics. They are contained in the randquantum Python module under the quantum directory.

stochastic - Simulation of common stochastic processes with native JavaScript

•    Javascript

Returns an array with num i.i.d normal random variables (http://en.wikipedia.org/wiki/Normal_distribution) of mean mu and standard deviation sigma. Parameters: mu is a real number, sigma is a strictly positive real number, and num is a positive integer (defaults to 1).

gostats - Statistics for go

•    Go

To install, do go get github.com/r0fls/gostats. Check out gostats_test.go for a working example of using each distribution. *note the negative binomial takes parameters r, p where r is the number allowed success before termination of the trials and p is the success of a given trial. The random variable produced by NegativeBinomial is the number of failures.

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