Showing 6 open source projects for "bayesian python"

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  • 1
    PyMC

    PyMC

    Bayesian Modeling and Probabilistic Programming in Python

    PyMC is a Python library for probabilistic programming focused on Bayesian statistical modeling and machine learning. Built on top of computational tools like Aesara and NumPy, PyMC allows users to define models using intuitive syntax and perform inference using MCMC, variational inference, and other advanced algorithms. It’s widely used in scientific research, data science, and decision modeling.
    Downloads: 5 This Week
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  • 2
    fesslix

    fesslix

    Stochastic Analysis

    A brief summary of the main features of Fesslix: - Perform non-intrusive reliability analysis or Bayesian updating either --- by running commands on the command line or --- by means of an Octave interface or --- by means of a Python interface - Flexible input language for writing Fesslix parameter files --- Control flow statements (e.g. if, for, while) --- Most parameters can be defined as functions - Working with response surfaces - Linear finite element analysis using truss, beam and plane stress/strain elements - Spectral Stochastic Finite Elements - Bayesian networks This is the download page for Windows executables of Fesslix.
    Downloads: 1 This Week
    Last Update:
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  • 3
    Provide a reference implementation of Moving Taylor Bayesian Regression, a method for nonparametric multi-dimensional function estimation with correlated errors from finite samples, as a Python package based on SciPy
    Downloads: 0 This Week
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  • 4
    Open Bayes is a python free/open library that allows users to easily create a bayesian network and perform inference/learning on it. It is mainly inspired from the Bayes Net Toolbox (BNT) but uses python as a base language. www.openbayes.org
    Downloads: 0 This Week
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  • 5
    This is a Python package for general Bayesian inference.
    Downloads: 0 This Week
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  • 6

    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.
    Downloads: 1 This Week
    Last Update:
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