DPMMSubClusters.jl
Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019) (by BGU-CS-VIL)
Stheno.jl
Probabilistic Programming with Gaussian processes in Julia (by JuliaGaussianProcesses)
DPMMSubClusters.jl | Stheno.jl | |
---|---|---|
1 | 2 | |
33 | 333 | |
- | 0.3% | |
0.0 | 4.3 | |
over 1 year ago | 7 months ago | |
Julia | Julia | |
GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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.
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.
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[R] Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
Julia - https://github.com/BGU-CS-VIL/DPMMSubClusters.jl
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.
What are some alternatives?
When comparing DPMMSubClusters.jl and Stheno.jl you can also consider the following projects:
QuantumFoca.jl - A repository for calculating Molecular Integrals, based on O-ohata method (1966) and Macmurchie-Davidson (1971)
MLJ.jl - A Julia machine learning framework
dpmmpythonStreaming - Python wrapper for the DPMMSubClusterStreaming.jl Julia package.
LsqFit.jl - Simple curve fitting in Julia
FLoops.jl - Fast sequential, threaded, and distributed for-loops for Julia—fold for humans™
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.
Gumbi - Gaussian Process Model Building Interface