SimulationBasedInference.jl aims to bring together a variety of different methods for simulation-based inference (SBI), i.e. statistical inference with simulator-like models, in the Julia programming language. Although SBI can be applied to both Bayesian and Frequentist inference frameworks, this package focuses on the Bayesian approach.
Please note that this package is still very much under construction and things may break or change without prior notice.
If you would like to use this package in your work, please let us know by creating an issue on GitHub or sending an email to brian.groenke@awi.de.
Simulator-type models are ubiquitous in science and engineering.
Most simulator-type models require some kind of input, e.g boundary conditions (forcings), physical properties or constants, etc.
Often, these parameters are not fully known a priori... but usually we know something!
Bayesian inference provides a natural framework for constraining this uncertainty using observed data:
The posterior distribution
Simulation-based inference (SBI) [1] refers to the problem of performing statistical inference (Bayesian or otherwise) of unknown parameters
is a dynamical model or physics-based simulator mapping from parameters to noisy (
There are two fundamental challenges with this problem:
- The forward model
$\mathcal{M}$ is very often nonlinear and typically has no closed-form solution. - Evaluating the forward map
$\mathcal{M}(\boldsymbol{\theta})$ is usually non-trivial, i.e. computationally expensive or at least inconvenient.
Thus, classical statistical methods that rely on either analytical or numerical methods to derive the posterior distribution are generally difficult (or impossible) to apply.
SimulationBasedInference extends the basic SciML interface to SBI problems.
The first basic building block is SimulatorForwardProblem, which wraps either an arbitrary function f(x), where x are the input parameters, or another SciMLProblem.
The purpose of the SimulatorForwardProblem abstraction is to provide a general interface for defining observables and interacting with the simulator.
To illustrate this, we start with the linear ODE example. We first define an ODEProblem describing the dynamical model:
ode_func(u,p,t) = -p[1]*u;
α_true = 0.2
ode_p = [α_true];
tspan = (0.0,10.0);
odeprob = ODEProblem(ode_func, [1.0], tspan, ode_p)We can then define observables of the system which should be sampled over the course of the simulation. In this case, we will define a trivial observable that just returns the state of the ODE integrator:
tsave = tspan[1]+0.1:0.2:tspan[end];
n_obs = length(tsave);
observable = SimulatorObservable(:y, integrator -> integrator.u, tspan[1], tsave, size(odeprob.u0), samplerate=0.01);We are now ready to construct a forward problem from odeprob and observable:
forward_prob = SimulatorForwardProblem(odeprob, observable)which can then be solved normally using the solve or init interface from SciML:
forward_sol = solve(forward_prob, ode_solver);
y_pred = get_observable(forward_sol, :y)╭────────────────────────────────╮
│ 50-element DimArray{Float64,1} │
├────────────────────────────────┴────────────────────────── dims ┐
↓ Ti Sampled{Float64} 0.1:0.2:9.9 ForwardOrdered Regular Points
└─────────────────────────────────────────────────────────────────┘
0.1 0.991057
0.3 0.961815
0.5 0.924101
0.7 0.887867
⋮
9.5 0.152753
9.7 0.146763
9.9 0.141009
For the purposes of demonstration, we construct artificial noisy observations from this forward solution:
true_obs = get_observable(forward_sol, :y)
noisy_obs = true_obs .+ 0.05*randn(rng, n_obs);The next step is defining a SimulatorInferenceProblem. We first define priors using Distributions.jl:
model_prior = prior(α=Beta(2,2));
noise_scale_prior = prior(σ=Exponential(0.1));then we assign a likelihood:
lik = IsotropicGaussianLikelihood(observable, noisy_obs, noise_scale_prior);and finally construct the inference problem:
inference_prob = SimulatorInferenceProblem(forward_prob, ode_solver, model_prior, lik);We can solve the inference problem with one of the simplest methods, ensemble importance sampling:
const rng = Random.MersenneTwister(1234);
enis_sol = solve(inference_prob, EnIS(), ensemble_size=128, rng=rng);
# note that for EnIS, the primary output of inference is the importance weights, and the ensemble reflects only draws from the prior.
importance_weights = get_weights(enis_sol);
prior_ens = get_transformed_ensemble(enis_sol);Note that all ensemble algorithms default to using the EnsembleThreads parallelization strategy. We could also use EnsembleDistributed or EnsembleSerial like so:
enis_sol = solve(inference_prob, EnIS(), EnsembleDistributed(), ensemble_size=128, rng=rng);To apply a different inference algorithm, such as EKS, we need only change the inference solver!
eks_sol = solve(inference_prob, EKS(), ensemble_size=128, rng=rng);If the PythonCall package is installed, we can also use one of the sequential neural density estimator methods from sbi:
# this defaults to the SNPE-C method, but this can be changed in the PySNE arguments.
snpe_sol = solve(inference_prob, PySNE(), num_simulations=1000, rng=rng);
# we can also re-use simulations from before instead of generating new ones:
simdata = SimulationBasedInference.sample_ensemble_predictive(enis_sol);
snpe_sol = solve(inference_prob, PySNE(), simdata, rng=rng);The following is a list of inference methods and/or probablisitc programming frameworks which are planned to be integrated into this package:
- Importance sampling, a.k.a particle batch smoothing [3] (PBS) and generalized likelihood uncertainty estimation [4] (GLUE)
- Particle Filtering and Sequential Monte Carlo [5,6] (PF/SMC)
- Ensemble Kalman sampling and inversion (EKS/EKI) via EnsembleKalmanProcesses.jl [7]
- Ensemble smoother with multiple data assimilation [8] (ES-MDA)
- Adaptive multiple importance sampling [9] (AMIS)
- Particle flow filters [10] (PFF)
- Sequential neural likelihood/posterior estimation (SNLE/SNPE) via sbi
- Calibrate, emulate, sample w/ Gaussian Processes [11] (CES-GP)
- Affine Invariant MCMC [12] (a.k.a "emcee") via AffineInvariantMCMC.jl
This work was supported by the Helmholtz Einstein Berlin International Research School in Data Science (grant HIDSS-0001) and the German Academic Exchange Service (grant 57647579).
[1] Cranmer, Kyle, Johann Brehmer, and Gilles Louppe. "The frontier of simulation-based inference." Proceedings of the National Academy of Sciences 117.48 (2020): 30055-30062.
[2] Evensen, Geir, et al. "Data Assimilation Fundamentals.", Springer (2022): https://doi.org/10.1007/978-3-030-96709-3
[3] Margulis, Steven, et al. "A Particle Batch Smoother Approach to Snow Water Equivalent Estimation." J. Hydrometeor. (2015): https://doi.org/10.1175/JHM-D-14-0177.1
[4] Beven, Keith, and Andrew Binley. "GLUE: 20 years on", Hydrol. Process. (2014): https://doi.org/10.1002/hyp.10082
[5] Peter Jan van Leeuwen. "Particle Filtering in Geophysical Systems". Mon. Wea. Rev. (2009): https://doi.org/10.1175/2009MWR2835.1
[6] Chopin, Nicolas, and Omiros Papaspiliopoulos. "An Introduction to Sequential Monte Carlo." Springer (2020): https://doi.org/10.1007/978-3-030-47845-2
[7] Dunbar, Oliver R. A. et al. "EnsembleKalmanProcesses.jl: Derivative-free ensemble-based model calibration." Journal of Open Source Software (2022): https://doi.org/10.21105/joss.04869
[8] Emerick, Alexandre A., and Albert C. Reynolds. "Ensemble smoother with multiple data assimilation." Computers & Geosciences (2013): https://doi.org/10.1016/j.cageo.2012.03.011
[9] Cornuet, Jean‐Marie, et al. "Adaptive multiple importance sampling." Scandinavian Journal of Statistics 39.4 (2012): 798-812.
[10] Hu, Chih‐Chi, and Peter Jan Van Leeuwen. "A particle flow filter for high‐dimensional system applications." Quarterly Journal of the Royal Meteorological Society 147.737 (2021): 2352-2374.
[11] Cleary, Emmet, et al. "Calibrate, emulate, sample." Journal of Computational Physics 424 (2021): 109716.
[12] Goodman, Jonathan, and Jonathan Weare. "Ensemble samplers with affine invariance." Communications in applied mathematics and computational science 5.1 (2010): 65-80.