Stheno.jl
Probabilistic Programming with Gaussian processes in Julia (by JuliaGaussianProcesses)
Gumbi
Gaussian Process Model Building Interface (by JohnGoertz)
| Stheno.jl | Gumbi | |
|---|---|---|
| 2 | 1 | |
| 335 | 48 | |
| 0.9% | - | |
| 4.3 | 5.2 | |
| 7 months ago | 6 months ago | |
| Julia | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stheno.jl
Posts with mentions or reviews of Stheno.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-19.
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[Discussion] Can we train with multiple sources of data, some very reliable, others less so?
There are multiple ways people do this. For example, you could use something like factor analysis, where the factor loadings onto the latent "true" signal/factor are fixed based on what you (presumably) know about the empirical reliability/error variance and (potentially) bias in each observed signal. Then you do your modeling with the inferred latent "true" signal. See the second example here to see that sort of approach in the context of a gaussian process model.
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Function prediction using Julia?
Another, more flexible (nonparametric) alternative might be to try a gaussian process model - for example, using Stheno.
Gumbi
Posts with mentions or reviews of Gumbi.
We have used some of these posts to build our list of alternatives
and similar projects.
What are some alternatives?
When comparing Stheno.jl and Gumbi you can also consider the following projects:
MLJ.jl - A Julia machine learning framework
mozregression - Regression range finder for Mozilla nightly builds
LsqFit.jl - Simple curve fitting in Julia
Machine-Learning - Implementation of different ML Algorithms from scratch, written in Python 3.x
GeoStats.jl - An extensible framework for geospatial data science and geostatistical modeling fully written in Julia
skbel - SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
DPMMSubClusters.jl - Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)