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: 1 This Week
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  • 2
    DEBay

    DEBay

    Deconvolutes qPCR data to estimate cell-type-specific gene expression

    DEBay: Deconvolution of Ensemble through Bayes-approach DEBay estimates cell type-specific gene expression by deconvolution of quantitative PCR data of a mixed population. It will be useful in experiments where the segregation of different cell types in a sample is arduous, but the proportion of different cell types in the sample can be measured. DEBay uses the population distribution data and the qPCR data to calculate the relative expression of the target gene in different cell types in...
    Downloads: 1 This Week
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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: 0 This Week
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