Search Results for "probability"
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Showing 12 open source projects for "probability"
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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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Random
Generation random numbers, words and elements
... - Random words: the application's vocabulary is about 10,000 words and will increase in future versions. - Random element: the number of elements in the list is not limited, all have an equal probability of selection. The application supports Russian and English languages. -
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Awesome Math
This is the Curriculum for "How to Learn Mathematics Fast"
This repository is a curated roadmap for learning the core mathematics used in computer science, machine learning, and data science without getting lost in unnecessary detours. It organizes topics like algebra, calculus, linear algebra, probability, and statistics into a pragmatic sequence that favors intuition and problem-solving over purely formal proofs. The materials emphasize short, high-leverage resources—video lectures, concise notes, and hands-on exercises—that help you build momentum quickly. It also suggests checkpoints and practice ideas so you can test comprehension and move forward with confidence. ... -
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ProbAbilistic CALculator - a package for computing with probability distributions
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SCRAM
Probabilistic Risk Assessment Tool
SCRAM is a free and open source probabilistic risk analysis tool. The tool is under development to include fault tree, event tree, common cause, and other standard analyses. -
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Cambridge Rocketry Simulator
Simulate high power rocket flights with splash down plots
...Parachute descent is also simulated. 3D flight trajectories are produced as well as detailed tabular flight data. Running in Monte Carlo mode allows generates multiple possible flight paths and splash down plots, indicating the probability of landing in an area. Peer-reviewed publication in the Journal of Open Research Software (JORS) http://doi.org/10.5334/jors.137 "Cambridge Rocketry Simulator – A Stochastic Six-Degrees-of-Freedom Rocket Flight Simulator" -
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fitGCP
Fitting genome coverage distributions with mixture models
...Making this information accessible can improve the quality of sequencing experiments and quantitative analyses. fitGCP is a framework for fitting mixtures of probability distributions to genome coverage profiles. Besides commonly used distributions, fitGCP uses distributions tailored to account for common artifacts. The mixture models are iteratively fitted based on the Expectation-Maximization algorithm. Please find the accompanying paper here: http://dx.doi.org/10.1093/bioinformatics/btt147 -
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Noisy Channel Simulator
Demonstrate errors in transmission of a file over a noisy channel.
This program was written to dimonstrate errors in transmission for a presentation on Claude Shannon's Noisy Channel Coding Theorem. It takes an input file, the probability of a bit being flipped, and, if specified, the size of the header of the file. The program was intended to take monochrome bitmap files as input, so that each bit refers to a pixel in the image and thus, it would be easy to see errors in the output file, as some of the pixels would be flipped; however, it will work on any input file. -
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pystats is a comprehensive Python module implementing algorithms for statistics and information theory, including probability distributions, descriptive statistics, analysis of variance, regression, and inference.
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RISO: distributed belief networks
Distributed, heterogeneous Bayesian belief networks
RISO: distributed, heterogeneous Bayesian belief networks. Belief network: a probability model defined on an acyclic directed graph; distributed: nodes can be on different hosts; and heterogeneous: allowing different types of conditional distributions. -
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C and Python code for basic probability and statistics contains classes for Combination, Permutation, and Cartesian Product.
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This is a python module that I wrote for statistics work. It can be used to calculate various probability distributions.
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