Search Results for "probability" - Page 2
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Showing 145 open source projects for "probability"
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Machine Learning Foundations
Machine Learning Foundations: Linear Algebra, Calculus, Statistics
...The project focuses on explaining the fundamental mathematical and computational concepts that underpin modern machine learning and artificial intelligence systems. The materials cover essential topics such as linear algebra, calculus, statistics, and probability, which form the theoretical basis of many machine learning algorithms. The repository includes Jupyter notebooks with explanations and examples that demonstrate how these mathematical principles relate to real machine learning applications. Each section introduces theoretical concepts and then illustrates them through practical coding examples to reinforce understanding. ... -
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Data Science Interviews
Data science interview questions and answers
...The project serves as a preparation resource for students, job seekers, and professionals who want to review the technical knowledge required for data science roles. The repository organizes questions into different categories including theoretical machine learning concepts, technical programming questions, and probability or statistics problems. Many of the questions cover fundamental machine learning topics such as linear models, decision trees, neural networks, and evaluation metrics. In addition to theoretical questions, the repository also includes practical interview topics related to coding challenges, SQL queries, and algorithmic thinking. -
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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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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 (https://www.juliansimon.com/) and Peter Bruce (https://www.scientificamerican.com/author/peter-bruce/) as a new way to teach Statistics to social science students. Of course, social science students... -
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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 -
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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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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. ... -
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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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MeasureTheory.jl
"Distributions" that might not add to one.
...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 to power and unify work on probabilistic programming and statistical computing within Julia. Check out our JuliaCon 2021 article detailing our ideas for and with this package. -
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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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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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Statistical Rethinking 2022
Statistical Rethinking course winter 2022
...The code emphasizes Bayesian data analysis using R, the rethinking package, and Stan models. It includes lecture code files, example datasets, and structured exercises that parallel the topics covered in the lectures (probability, regression, model comparison, Bayesian updating). The repo functions as a direct hands-on reference for students following the 2022 recorded lecture series. There are 10 weeks of instruction. Links to lecture recordings will appear in this table. Weekly problem sets are assigned on Fridays and due the next Friday, when we discuss the solutions in the weekly online meeting. -
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...The trade simulator autoselects the indicator parameters giving the best profit for a each indicator type. The combination of the indicator reversal signals and the detected candlestick patterns returns the overall probability for a purchase or disposal. The indicators computed with the best profit parameters will be shown on different analysis charts.
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ensemble-verification-system
A software tool for verifying ensemble forecasts
The Ensemble Verification System (EVS) is designed to verify ensemble forecasts of hydrologic and hydrometeorological variables, such as temperature, precipitation, and streamflow, issued at discrete forecast locations (points or areas). -
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Statistics for Data Scientists
"Statistics for Data Scientists: 50 Essential Concepts"
The “statistics-for-data-scientists” repository is a pedagogical resource designed to bridge rigorous statistics theory and practical data science workflows. The code and materials are intended to help data scientists and analysts grasp statistical principles (e.g. inference, regressions, hypothesis testing, probability, confidence intervals) in contexts relevant to real data analysis tasks. The repository includes Jupyter notebooks, R scripts, worked examples, and possibly problem sets that illustrate how statistical methods are applied to real datasets. It aims to demystify the bridge between textbook statistics and empirical modeling by walking through assumption checking, visualization, interpreting outputs, and pitfalls of misuse. ... -
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stat-cookbook
The probability and statistics cookbook
A compact “Probability and Statistics Cookbook” offering concise mathematical recipes for key statistical concepts—expectation, variance, distributions and inequalities—packaged as LaTeX and R-based executable documents. -
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openbu
Lightweight L5e vehicle with freely accessible engineering documents
...Used Software: - LibreOffice - Free CAD + FEM Workbench + CFD OpenFoam Workbench - Z88 Arion (topology optimization, Freeware) - KiCAD - Gimp - Arduino IDE - CooCox To make the descissions in this project as transparent as possible, all big engineering descissions are evaluated with the following criteria: - efficency - simplicity - safety - maintenance - manufacturability - failure probability - clean and good looking design -
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ACToolkit's temporary homepage
Max-objects-&-patches for Algorithmic Composition and statistical DSP
...The name of our package ACToolkit is a derivative of AC Toolbox, the legendary "Algorithmic music composition program for macOS" of Mr Paul Berg's (https://www.actoolbox.net). Our Max-external-objects feature the real-time realisation of discrete-continuous Probability Distribution Functions, the PDFs derived from sets of interpolated discrete-probability-densities, on random numbers. Also, the package includes numerous examples exemplifying the methodologies on the usage of PDF-shaping in Algorithmic Composition, especially ones involving signal processing techniques. -
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StatisticsTables
This is a Java APP for Probability and Statistics Tables
If you have Windows or a MAC , you can setup and run Statistics Tables with very little effort. You can use jdistabs in place of paper Probability Tables to get electronic probabilities. You can also download the Java code used to create this APP. There are 2 zips. One is for a working windows APP, and the other with complete code and file structure. I used Eclipse to develop the APP. Click the link you want to download to get the Statistics Tables Windows.exe or OS X zipped files. ... -
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Enterprise Calendaring-Journal
Hourly Activity While Sitting On Sensitive Workflow
Written in Java, this fragments of code demonstrate any highly sensitive works & environment to maintain the balance of probability of work and life through mix of journaling, calendaring and cognitive work mixes. Just privileged to upload my research idea of logging executive tasks in a systematic and structured light-weight way! -
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Stats With Julia Book
Collection of runnable Julia code examples for a statistics book
StatsWithJuliaBook is the companion code repository for the book Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. It contains over 200 code blocks that correspond to the book’s ten chapters and three appendices, covering topics from probability theory and data summarization to regression analysis, hypothesis testing, and machine learning basics. The repository is designed for Julia users and provides ready-to-run examples that reinforce theoretical concepts with practical implementation. Readers can explore how Julia supports statistical modeling, simulation, and computational methods in data science workflows. ... -
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Big List of Naughty Strings
List of strings which have a high probability of causing issues
The Big List of Naughty Strings is a community-maintained catalog of “gotcha” inputs that commonly break software, from unusual Unicode to SQL and script injection payloads. It exists so developers and QA engineers can easily test edge cases that normal test data would miss, such as zero-width characters, right-to-left marks, emojis, foreign alphabets, and long or malformed strings. By throwing these strings at forms, APIs, databases, and UIs, teams can discover encoding bugs, sanitizer... -
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SMTracker (v1.5, v2.0)
A tool for analysis and visualization of single-molecule tracking data
SMTracker v2.0 is a MATLAB-based graphical user interface (GUI) for automatically quantifying, visualising and managing SMT data via five interactive panels, allowing the user to interactively explore tracking data from several conditions, movies and cells on a track-by- track basis. Diffusion parameters and motion behaviour is analysed by several methods: a) by a Gaussian mixture model ,or b) by using the cumulative probability distribution of square displacements, c) Mean-Squared displacement fits, d) by Jump Distance analysis. It also includes exploratory tools to visualise single trajectories or dynamic heat maps. -
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LaPath
Learning Automata algorithm for the shortest path problem.
...In the context of network routing, LA residing at intermediate nodes along a path, exploit feedback from the destination node for reducing, e.g., path's length. According to topology’s resources like the node and edge numbers, the proper number of iterations must be used. More iterations lead to paths with higher probability of being optimal but more computing resources are consumed. Development takes place at https://github.com/zfoxer/LaPath