DiffEqBayes

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

https://github.com/sciml/diffeqbayes.jl

Keywords

bayesian-inference dae dde differential-equations julia neural-differential-equations neural-ode ode ordinary-differential-equations probabilistic-programming scientific-ai scientific-machine-learning scientific-ml sciml sde stan stochastic-differential-equations

Keywords from Contributors

matrix-exponential pde julialang wiener-process partial-differential-equations neural-sde brownian-motion noise-processes stochastic-processes krylov-subspace-methods

Last synced: 6 months ago · JSON representation ·

Repository

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

Basic Info

Host: GitHub
Owner: SciML
License: other
Language: Julia
Default Branch: master
Homepage: https://docs.sciml.ai/DiffEqBayes/stable/
Size: 41.5 MB

Statistics

Stars: 125
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Forks: 28
Open Issues: 16
Releases: 62

Topics

bayesian-inference dae dde differential-equations julia neural-differential-equations neural-ode ode ordinary-differential-equations probabilistic-programming scientific-ai scientific-machine-learning scientific-ml sciml sde stan stochastic-differential-equations

Created over 8 years ago · Last pushed 6 months ago

Metadata Files

Readme License Citation

README.md

DiffEqBayes.jl

This repository is a set of extension functionality for estimating the parameters of differential equations using Bayesian methods. It allows the choice of using CmdStan.jl, Turing.jl, DynamicHMC.jl and ApproxBayes.jl to perform a Bayesian estimation of a differential equation problem specified via the DifferentialEquations.jl interface.

To begin you first need to add this repository using the following command.

julia Pkg.add("DiffEqBayes") using DiffEqBayes

Tutorials and Documentation

For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation, which contains the unreleased features.

Example

```julia using ParameterizedFunctions, OrdinaryDiffEq, RecursiveArrayTools, Distributions f1 = @ode_def LotkaVolterra begin dx = a * x - x * y dy = -3 * y + x * y end a

p = [1.5] u0 = [1.0, 1.0] tspan = (0.0, 10.0) prob1 = ODEProblem(f1, u0, tspan, p)

σ = 0.01 # noise, fixed for now t = collect(1.0:10.0) # observation times sol = solve(prob1, Tsit5()) priors = [Normal(1.5, 1)] randomized = VectorOfArray([(sol(t[i]) + σ * randn(2)) for i in 1:length(t)]) data = convert(Array, randomized)

using CmdStan #required for using the Stan backend bayesianresultstan = stan_inference(prob1, t, data, priors)

bayesianresultturing = turing_inference(prob1, Tsit5(), t, data, priors)

using DynamicHMC #required for DynamicHMC backend bayesianresulthmc = dynamichmc_inference(prob1, Tsit5(), t, data, priors)

bayesianresultabc = abc_inference(prob1, Tsit5(), t, data, priors) ```

Using save_idxs to declare observables

You don't always have data for all of the variables of the model. In case of certain latent variables you can utilise the save_idxs kwarg to declare the observed variables and run the inference using any of the backends as shown below.

```julia sol = solve(prob1, Tsit5(), save_idxs = [1]) randomized = VectorOfArray([(sol(t[i]) + σ * randn(1)) for i in 1:length(t)]) data = convert(Array, randomized)

using CmdStan #required for using the Stan backend bayesianresultstan = staninference(prob1, t, data, priors, saveidxs = [1])

bayesianresultturing = turinginference(prob1, Tsit5(), t, data, priors, saveidxs = [1])

using DynamicHMC #required for DynamicHMC backend bayesianresulthmc = dynamichmcinference(prob1, Tsit5(), t, data, priors, saveidxs = [1])

bayesianresultabc = abcinference(prob1, Tsit5(), t, data, priors, saveidxs = [1]) ```

Owner

Name: SciML Open Source Scientific Machine Learning
Login: SciML
Kind: organization
Email: contact@chrisrackauckas.com

Website: https://sciml.ai
Twitter: SciML_Org
Repositories: 170
Profile: https://github.com/SciML

Open source software for scientific machine learning

Citation (CITATION.bib)

@article{DifferentialEquations.jl-2017,
 author = {Rackauckas, Christopher and Nie, Qing},
 doi = {10.5334/jors.151},
 journal = {The Journal of Open Research Software},
 keywords = {Applied Mathematics},
 note = {Exported from https://app.dimensions.ai on 2019/05/05},
 number = {1},
 pages = {},
 title = {DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia},
 url = {https://app.dimensions.ai/details/publication/pub.1085583166 and http://openresearchsoftware.metajnl.com/articles/10.5334/jors.151/galley/245/download/},
 volume = {5},
 year = {2017}
}

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Development Distribution Score (DDS): 0.688

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David Widmann	d****n	21
Ayush Pandey	a**p@g**m	18
Yao Lu	l**s@g**m	18
Arno Strouwen	a**n@t**e	10
dependabot[bot]	4****]	9
abhinavgupta768	a**8@g**m	8
Saumil Shah	s**h@e**e	7
Kai Xu	x**0@g**m	5
Mauro Werder	m**c@r**m	5
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Sasha Petrenko	s**5@u**u	3
mohamed82008	m**8@g**m	3
Chris de Graaf	me@c****v	3
Saumil Shah	s**7@g**m	2
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Total packages: 1
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Total dependent packages: 1
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Total versions: 52

juliahub.com: DiffEqBayes

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

Homepage: https://docs.sciml.ai/DiffEqBayes/stable/
Documentation: https://docs.juliahub.com/General/DiffEqBayes/stable/
License: MIT
Latest release: 3.9.0
published 8 months ago

Versions: 52
Dependent Packages: 1
Dependent Repositories: 0
Downloads: 18 Total

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Forks count: 5.8%

Stargazers count: 6.3%

Dependent repos count: 9.9%

Average: 11.2%

Dependent packages count: 23.0%

Last synced: 6 months ago

DiffEqBayes

Science Score: 54.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

DiffEqBayes.jl

Tutorials and Documentation

Example

Using save_idxs to declare observables

Owner

Citation (CITATION.bib)

GitHub Events

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