Showing 5 open source projects for "regression"

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  • 1
    MultivariateStats.jl

    MultivariateStats.jl

    A Julia package for multivariate statistics and data analysis

    A Julia package for multivariate statistics and data analysis (e.g. dimensionality reduction).
    Downloads: 0 This Week
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    Bayesian Statistics

    Bayesian Statistics

    This repository holds slides and code for a full Bayesian statistics

    This repository holds slides and code for a full Bayesian statistics graduate course. Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used...
    Downloads: 0 This Week
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  • 3
    SymbolicRegression.jl

    SymbolicRegression.jl

    Distributed High-Performance Symbolic Regression in Julia

    SymbolicRegression.jl searches for symbolic expressions which optimize a particular objective.
    Downloads: 2 This Week
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  • 4
    LIBSVM.jl

    LIBSVM.jl

    LIBSVM bindings for Julia

    LIBSVM bindings for Julia. This is a Julia interface for LIBSVM and for the linear SVM model provided by LIBLINEAR. Supports all LIBSVM models: classification C-SVC, nu-SVC, regression: epsilon-SVR, nu-SVR and distribution estimation: one-class SVM. Model objects are represented by Julia-type SVM which gives you easy access to model features and can be saved e.g. as JLD file.
    Downloads: 0 This Week
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  • 5
    AugmentedGaussianProcesses.jl

    AugmentedGaussianProcesses.jl

    Gaussian Process package based on data augmentation, and sparsity

    AugmentedGaussianProcesses.jl is a Julia package in development for Data Augmented Sparse Gaussian Processes. It contains a collection of models for different gaussian and non-gaussian likelihoods, which are transformed via data augmentation into conditionally conjugate likelihood allowing for extremely fast inference via block coordinate updates. There are also more options to use more traditional variational inference via quadrature or Monte Carlo integration.
    Downloads: 0 This Week
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