Stheno.jl
skbel
| Stheno.jl | skbel | |
|---|---|---|
| 2 | 1 | |
| 335 | 20 | |
| 0.9% | - | |
| 4.3 | 2.4 | |
| 7 months ago | 3 months ago | |
| Julia | Python | |
| GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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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.
skbel
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Predicting the distribution of a variable rather than a point estimate
For multivariate regression, I'm developing a Python package called SKBEL.
What are some alternatives?
MLJ.jl - A Julia machine learning framework
PFASimplu - Aplicatie/soft pentru cei care tin contabilitatea in partida simpla (PFA/II, etc)
LsqFit.jl - Simple curve fitting in Julia
pymc-resources - PyMC educational resources
GeoStats.jl - An extensible framework for geospatial data science and geostatistical modeling fully written 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 ;)
DPMMSubClusters.jl - Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)
lasio - Python library for reading and writing well data using Log ASCII Standard (LAS) files
Gumbi - Gaussian Process Model Building Interface
GPflow - Gaussian processes in TensorFlow