DPMMSubClusters.jl VS Stheno.jl

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

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

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 Stheno.jl
1 2
33 335
- 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.
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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.

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.

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

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