Multi-output Gaussian processes have recently gained strong attention from researchers and have become an active research topic in machine learning’s multi-task learning. The advantage of multi-output Gaussian processes is their capacity to simultaneously learn and infer many outputs which have a similar source of uncertainty from inputs.
This talk presents to audiences how to build multi-output Gaussian processes in PyMC. It first introduces the concept of Gaussian processes (GPs) and multi-output GPs and how they can address real problems in several domains. It then shows how to implement multi-output GPs models such as the intrinsic coregionalization model (ICM) and the linear model of coregionalization (LCM) in Python using PyMC with real-world datasets.
| Notebooks/Slides | Content | Link |
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| 1. Intro to PyMC GP | This notebook introduces the basic concept of Gaussian Process (GP)
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| 2. Intro to multi-output GPs | This slides introduce Multi-output Gaussian Processes (MoGPs)
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| 3. PyMC MoGP example | This notebook demonstrates how to implement a Multi-output Gaussian Process (MoGP) in PyMC | |
Please create a new cell at the beginning of the notebooks and run these following commands to install the latest PyMC version and related packages
!pip install pymc==5.0.2 # the latest pymc version
!pip install graphviz
!pip install matplotlib==3.6.3
!pip install watermark
Set up the environment
git clone git@github.com:danhphan/workshops.git
cd ./workshops/2023-PyMCon
conda create --name pymcon -c conda-forge python=3.9
conda activate pymcon
conda install -c conda-forge mamba
mamba install -c conda-forge "pymc>=5.0.0"
pip install -r requirements.txt