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It handles model parameterization and training, allowing users to focus on the\ndata and results without being distracted by tedious and repetitive tasks.\n\n\u003e [!NOTE]  \n\u003e This is an experimental package and may undergo breaking changes.\n\n## Usage\n\nIn order to fit a GP, define one according to the familiar AbstractGP.jl interface and\nlet EasyGPs.jl handle the rest. The entry point for this is `EasyGPs.fit` (not exported):\n\n```julia\nusing EasyGPs\n\nkernel = 1.0 * with_lengthscale(SEKernel(), 1.0)\ngp = with_gaussian_noise(GP(0.0, kernel), 0.1)\nx = 0:0.1:10\ny = sin.(x) .+ 0.1 .* randn(length(x))\nfitted_gp = EasyGPs.fit(gp, x, y)\n```\n\nUnder the hood, this will recognize the parameters (mean, variance, lengthscale) of the `GP`\nyou defined and automatically construct a parameterized model. It will then choose a cost\nfunction, optimizer, and AD backend, and determine the optimal parameters.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjuliagaussianprocesses%2Feasygps.jl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjuliagaussianprocesses%2Feasygps.jl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjuliagaussianprocesses%2Feasygps.jl/lists"}