The Julia Language project
Stay organized with collections
Save and categorize content based on your preferences.
This page contains the details of a technical writing project accepted for
Google Season of Docs.
Project summary
- Open source organization:
- The Julia Language
- Technical writer:
- Liza
- Project name:
- Bayesian inference for Gaussian Processes
- Project length:
- Standard length (3 months)
Project description
I would like to develop (and teach myself) some easy-to-start material, allowing to perform Bayesian inference for Gaussian processes (GPs) using Julia's ecosystem.
Outline:
- What are non-parameteric models and, in particular, GPs
- One-dimensional curve fitting simple example, i.e. given a set of pairs (x_i, y_i) how to fit f(x)=y
- Discuss different kernels: squared exponential, Matern, linear, compositions
- A more involved 2d example, modelling spatial data
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2024-11-08 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-11-08 UTC."],[[["This Google Season of Docs project focuses on creating introductory materials for Bayesian inference for Gaussian Processes using the Julia programming language."],["The project will cover fundamental concepts of non-parametric models and Gaussian Processes, along with practical examples of curve fitting and spatial data modeling."],["Various kernel functions, such as squared exponential, Matern, and linear, will be explored to demonstrate their impact on model performance."],["The project aims to provide accessible resources for users to learn and apply Bayesian inference techniques with Gaussian Processes in Julia."]]],["The project, titled \"Bayesian inference for Gaussian Processes,\" involves creating educational material on using Julia's ecosystem for Bayesian inference with Gaussian processes (GPs). The project will cover non-parametric models, one-dimensional curve fitting examples with various kernels (squared exponential, Matern, linear), and a 2D spatial data modeling example. It will be developed by technical writer Liza over a standard 3-month period for The Julia Language open-source organization.\n"]]