Showing 6 open source projects for "bayesian c#"

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  • 1
    PyMC3

    PyMC3

    Probabilistic programming in Python

    PyMC3 allows you to write down models using an intuitive syntax to describe a data generating process. Fit your model using gradient-based MCMC algorithms like NUTS, using ADVI for fast approximate inference — including minibatch-ADVI for scaling to large datasets, or using Gaussian processes to build Bayesian nonparametric models. PyMC3 includes a comprehensive set of pre-defined statistical distributions that can be used as model building blocks. Sometimes an unknown parameter or variable...
    Downloads: 1 This Week
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  • 2
    JAGS is Just Another Gibbs Sampler. It is a program for the statistical analysis of Bayesian hierarchical models by Markov Chain Monte Carlo.
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    Downloads: 1,977 This Week
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  • 3
    Habfuzz

    Habfuzz

    A command-line tool for data-driven fuzzy modelling

    Input 1 - A training dataset (multiple observations) of up to four variables (predictors) against one (response variable) Input 2 - A test dataset (multiple observations) of the same four variables with unknown response variable Output - Calculation of the response variable for each test observation using fuzzy logic or fuzzy rule-based Bayesian algorithms HABFUZZ is a habitat model, which can be used in ecohydraulic modelling applications for the calculation of the instream habitat...
    Downloads: 2 This Week
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  • 4
    An implementation of memory-prediction framework applied for image recognition. Based on Jeff Hawkins' book On Intelligence. It models the high-level hierarchical architecture of human neocortex and uses Bayesian belief revision for making predictions
    Downloads: 4 This Week
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  • 5
    A C++ library for Bayesian computation, including a collection of more generally-applicable utilities.
    Downloads: 1 This Week
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  • 6
    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. The particle based inference can answer arbitrary...
    Downloads: 0 This Week
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