Posts tagged gaussian process

Multi-output Gaussian Processes: Coregionalization models using Hamadard product

This notebook shows how to implement the Intrinsic Coregionalization Model (ICM) and the Linear Coregionalization Model (LCM) using a Hamadard product between the Coregion kernel and input kernels. Multi-output Gaussian Process is discussed in this paper by Bonilla et al. [2007]. For further information about ICM and LCM, please check out the talk on Multi-output Gaussian Processes by Mauricio Alvarez, and his slides with more references at the last page.

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Kronecker Structured Covariances

PyMC contains implementations for models that have Kronecker structured covariances. This patterned structure enables Gaussian process models to work on much larger datasets. Kronecker structure can be exploited when

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Gaussian Processes using numpy kernel

Example of simple Gaussian Process fit, adapted from Stan’s example-models repository.

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Gaussian Process for CO2 at Mauna Loa

top-level ‘substitutions’ key is deprecated, place under ‘myst’ key instead [myst.topmatter]

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Mean and Covariance Functions

A large set of mean and covariance functions are available in PyMC. It is relatively easy to define custom mean and covariance functions. Since PyMC uses Aesara, their gradients do not need to be defined by the user.

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