Showing 5 open source projects for "bayesian"

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  • 1
    Bayesian Methods for Hackers

    Bayesian Methods for Hackers

    An introduction to Bayesian methods + probabilistic programming

    Bayesian Methods for Hackers is the source repository for Bayesian Methods for Hackers, an educational book about Bayesian inference and probabilistic programming. It is written from a computation-first perspective, prioritizing intuition, examples, and executable notebooks over heavy mathematical formalism. The project introduces readers to uncertainty, Bayesian modeling, MCMC, priors, posteriors, and real-world probabilistic reasoning.
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  • 2
    Think Bayes

    Think Bayes

    Code repository for Think Bayes

    ThinkBayes is the code repository accompanying Think Bayes: a book on Bayesian statistics written in a computational style. Instead of heavy focus on continuous mathematics or calculus, the book emphasizes learning Bayesian inference by writing Python programs. The project includes code examples, scripts, and environments that correspond to the chapters of the book. Learners can run the code, experiment with probability distributions, compute posterior probabilities, and understand Bayesian updating via simulation and algorithmic methods. ...
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  • 3
    faif

    faif

    C++ header only library with AI and bioinformatics algorithms

    C++ header only library, small and fast; Naive Bayesian Classifier, Decision Tree Classifier (ID3), DNA/RNA nucleotide second structure predictor, timeseries management, timeseries prediction, generic Evolutionary Algorithm, generic Hill Climbing algorithm and others.
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  • 4
    Java/XML toolkit for research using Bayesian networks and other graphical models of probability (exact and approximate inference, structure learning, etc.)
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  • 5
    DBNL

    DBNL

    Dynamic Bayesian Network Library

    DBNL is a cross-platform library that offers a variety of implementations of Bayesian networks and machine learning algorithms. It is a flexible library that covers all aspects of Bayesian netwoks from representation to reasoning and learning. It allows you to create simple static networks as well as complex temporal models with changing structure. It can handle highly non-linear dependencies between multivariate random variables.
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