skbel VS Stheno.jl

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

skbel

SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn. (by robinthibaut)
hydrogeology geology groundwater pfa Machine Learning bayesian-inference gaussian-processes gaussian-process gaussian-process-regression Sklearn multiple-output-regression multivariate-regression
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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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skbel Stheno.jl
1 2
20 335
- 0.9%
2.4 4.3
3 months ago 7 months ago
Python Julia
BSD 3-clause "New" or "Revised" License 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.
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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.

skbel

Posts with mentions or reviews of skbel. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-14.

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 skbel and Stheno.jl you can also consider the following projects:

PFASimplu - Aplicatie/soft pentru cei care tin contabilitatea in partida simpla (PFA/II, etc)

MLJ.jl - A Julia machine learning framework

pymc-resources - PyMC educational resources

LsqFit.jl - Simple curve fitting in Julia

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers - aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

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

lasio - Python library for reading and writing well data using Log ASCII Standard (LAS) files

DPMMSubClusters.jl - Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)

GPflow - Gaussian processes in TensorFlow

Gumbi - Gaussian Process Model Building Interface

skbel vs PFASimplu Stheno.jl vs MLJ.jl skbel vs pymc-resources Stheno.jl vs LsqFit.jl skbel vs Probabilistic-Programming-and-Bayesian-Methods-for-Hackers Stheno.jl vs GeoStats.jl skbel vs lasio Stheno.jl vs DPMMSubClusters.jl skbel vs GPflow Stheno.jl vs Gumbi