Howdy folks,

GPyTorch provides Gaussian likelihood objects for fixed noise (FixedNoiseGaussianLikelihood) and for multi-task models (MultitaskGaussianLikelihood). I was wondering if someone could provide me some guidance on how to get a fixed noise multi-task Gaussian likelihood?

Thanks in advance

Galto

Feature request

In several places we use multiple dispatch. Right now the types to dispatch on are configured separately. Could we infer the types to dispatch on from Python type hints? That would simplify the code.

Motivation

Instead of:

@dispatch.expectation.register(Gaussian, kernels.Sum, InducingPoints, NoneType, NoneType)
def expectation_gaussian_sum_inducingpoints(
    

The Makie.jl ecosystem is mature enough to support plot recipes. Migrating to this new plotting ecosystem will enable high-performance 3D visualisations on the GPU.

• SimpleMesh
• CartesianGrid
• PointSet
• GeometrySet
• Views
• Partitions
• GeoStatsBase.jl
• Variography.jl

There are a variety of interesting optimisations that can be performed on kernels of the form

k(x, z) = w_1 * k_1(x, z) + w_2 * k_2(x, z) + ... + w_L k_L(x, z)

A naive recursive implementation in terms of the current Sum and Scaled kernels hides opportunities for parallelism in the computation of each term, and the summation over terms.

Notable examples of kernels with th

We need to use ChainCoreTestUtils to test our new rrule/frule