GeoStats.jl
Stheno.jl
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| GeoStats.jl | Stheno.jl | |
|---|---|---|
| 1 | 2 | |
| 490 | 332 | |
| 2.4% | 0.3% | |
| 8.8 | 4.3 | |
| 20 days ago | 6 months ago | |
| Julia | Julia | |
| MIT License | GNU General Public License v3.0 or later |
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.
GeoStats.jl
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MLH Fellowship: Memories that I always love to bring back.
Yeah, after all this. It's ok to have this thought in mind. I will try to keep this brief as the blog is about the fellowship experience. All of us were assigned to various projects and we were actively working on them at the same time. One thing I liked, in particular, was flexible work hours. There is no constraint to when a fellow must work on the project. By the end of the Fellowship, everyone is supposed to complete the tasks assigned to us. I am assigned to project GeoStats.jl which is currently under the JuliaEarth organization. I worked on it with the help of an amazing maintainer Julio Hoffimann and was able to successfully get by PR merged.
Stheno.jl
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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.
What are some alternatives?
3d-tiles - Specification for streaming massive heterogeneous 3D geospatial datasets :earth_americas:
MLJ.jl - A Julia machine learning framework
ComponentArrays.jl - Arrays with arbitrarily nested named components.
LsqFit.jl - Simple curve fitting in Julia
Geodesy.jl - Work with points defined in various coordinate systems.
skbel - SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
NumericalAlgorithms.jl - [DEPRECATED] Statistics & Numerical algorithms implemented in Julia.
DPMMSubClusters.jl - Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)
RSCalibration - Docs and scripts to estimate a camera's rolling shutter readout time
Gumbi - Gaussian Process Model Building Interface
GeoStatsBase.jl - Base package for the GeoStats.jl framework
CovidAid - 🔗 Community driven platform for Connecting communities through Intergenerational collaboration