The basic Gaussian Process object. :param kernel: An instance of a subclass of :class:`kernels.Kernel`. If no kernel is spec

r""" This kernel returns the constant .. math:: k(\mathbf{x}_i,\,\mathbf{x}_j) = c where :math:`c` is a parameter. :param

A simple concrete model with a single parameter ``value`` Args: value (float): The value of the model.

This is the most basic solver built using :func:`scipy.linalg.cholesky`. kernel (george.kernels.Kernel): A subclass of :class:`Kernel` speci

r""" The simplest periodic kernel. This .. math:: k(\mathbf{x}_i,\,\mathbf{x}_j) = \cos\left( \frac{2\,\pi\,|x_i - x_j|}{

r""" The dot product kernel .. math:: k(\mathbf{x}_i,\,\mathbf{x}_j) = \mathbf{x}_i \cdot \mathbf{x}_j with no parameters.

r""" This kernel is a no-op

r""" The exponential kernel is a stationary kernel where the value at a given radius :math:`r^2` is given by: .. math:: k(r^2) =

r""" The exp-sine-squared kernel is a commonly used periodic kernel. Unlike the :class:`CosineKernel`, this kernel never has negative covarian

r""" The exponential-squared kernel is a stationary kernel where the value at a given radius :math:`r^2` is given by: .. math::

r""" A solver using `Sivaram Amambikasaran's HODLR algorithm <http://arxiv.org/abs/1403.6015>`_ to approximately solve the GP linear algeb

The abstract kernel type. Every kernel implemented in George should be a subclass of this object.

r""" The linear regression kernel .. math:: k(\mathbf{x}_i,\,\mathbf{x}_j) = \frac{(\mathbf{x}_i \cdot \mathbf{x}_j)^P}{

r""" A local Gaussian kernel. .. math:: k(\mathbf{x}_i,\,\mathbf{x}_j) = \exp\left( -\frac{(x_i - x_0)^2 + (x_j - x_0)^2}

r""" The Matern-3/2 kernel is stationary kernel where the value at a given radius :math:`r^2` is given by: .. math:: k(r^2) = \l

r""" The Matern-5/2 kernel is stationary kernel where the value at a given radius :math:`r^2` is given by: .. math:: k(r^2) = \l

This is an abstract metric base class. The subclasses have all the good stuff.

An abstract class implementing the skeleton of the modeling protocol Initial parameter values can either be provided as arguments in the sam

An abstract wrapper for combining named :class:`Model` objects The parameter names of a composite model are prepended by the submodel na

r""" A polynomial kernel .. math:: k(\mathbf{x}_i,\,\mathbf{x}_j) = (\mathbf{x}_i \cdot \mathbf{x}_j + \sigma^2)^P

r""" This is equivalent to a "scale mixture" of :class:`ExpSquaredKernel` kernels with different scale lengths drawn from a gamma distribution