GitHub topics: gaussian-process-optimisation
stk-kriging/stk
The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on the interpolation/regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior.
Language: MATLAB - Size: 5.91 MB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 38 - Forks: 13
Akatsuki96/adabkb
Implementation of Ada-BKB a scalable Gaussian Process bandit optimization algorithm
Language: Python - Size: 9.67 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 3 - Forks: 0
compops/gpo-ifac2014
Particle filter-based Gaussian process optimisation for parameter inference
Language: Matlab - Size: 25.4 KB - Last synced at: almost 2 years ago - Pushed at: over 7 years ago - Stars: 9 - Forks: 6
compops/gpo-smc-abc
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
Language: Python - Size: 11.7 MB - Last synced at: almost 2 years ago - Pushed at: over 7 years ago - Stars: 11 - Forks: 6
kjanjua26/Gaussian_Processes_Playground
This is a repository for implementing various Gaussian Processes (GPs) and also some notes regarding GPs from different lectures.
Language: Jupyter Notebook - Size: 33.9 MB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 0
