Stheno.jl VS DPMMSubClusters.jl

Compare Stheno.jl vs DPMMSubClusters.jl and see what are their differences.

Stheno.jl

Probabilistic Programming with Gaussian processes in Julia (by JuliaGaussianProcesses)
bayesian-inference probabilistic-programming approximate-inference gaussian-processes Machine Learning
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DPMMSubClusters.jl

Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019) (by BGU-CS-VIL)
mixture-model Machine Learning dirichlet-process dirichlet-process-mixtures gaussian multinomial bayesian-inference Inference Julia Distributed parallel-computing Distributed Computing ccgrid hpml hyper-computing
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Stheno.jl DPMMSubClusters.jl
2 1
335 33
0.9% -
4.3 0.0
7 months ago over 1 year ago
Julia Julia
GNU General Public License v3.0 or later GNU General Public License v3.0 only
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.

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.
  • [Discussion] Can we train with multiple sources of data, some very reliable, others less so?
    1 project | /r/MachineLearning | 10 Nov 2022
    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.
  • Function prediction using Julia?
    2 projects | /r/Julia | 19 Jan 2022
    Another, more flexible (nonparametric) alternative might be to try a gaussian process model - for example, using Stheno.

DPMMSubClusters.jl

Posts with mentions or reviews of DPMMSubClusters.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-07.

What are some alternatives?

When comparing Stheno.jl and DPMMSubClusters.jl you can also consider the following projects:

MLJ.jl - A Julia machine learning framework

QuantumFoca.jl - A repository for calculating Molecular Integrals, based on O-ohata method (1966) and Macmurchie-Davidson (1971)

LsqFit.jl - Simple curve fitting in Julia

dpmmpythonStreaming - Python wrapper for the DPMMSubClusterStreaming.jl Julia package.

GeoStats.jl - An extensible framework for geospatial data science and geostatistical modeling fully written in Julia

FLoops.jl - Fast sequential, threaded, and distributed for-loops for Julia—fold for humans™

skbel - SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.

Gumbi - Gaussian Process Model Building Interface

Stheno.jl vs MLJ.jl DPMMSubClusters.jl vs QuantumFoca.jl Stheno.jl vs LsqFit.jl DPMMSubClusters.jl vs dpmmpythonStreaming Stheno.jl vs GeoStats.jl DPMMSubClusters.jl vs FLoops.jl Stheno.jl vs skbel Stheno.jl vs Gumbi