sbi
: simulationbased inference¶
sbi
: A Python toolbox for simulationbased inference.
Inference can be run in a single line of code:
posterior = infer(simulator, prior, method='SNPE', num_simulations=1000)

To learn about the general motivation behind simulationbased inference, and the inference methods included in
sbi
, read on below. 
For example applications to canonical problems in neuroscience, browse the recent preprint Training deep neural density estimators to identify mechanistic models of neural dynamics.

If you want to get started using
sbi
on your own problem, jump to installation and then check out the tutorial.
Motivation and approach¶
Many areas of science and engineering make extensive use of complex, stochastic, numerical simulations to describe the structure and dynamics of the processes being investigated.
A key challenge in simulationbased science is constraining these simulation models’ parameters, which are intepretable quantities, with observational data. Bayesian inference provides a general and powerful framework to invert the simulators, i.e. describe the parameters which are consistent both with empirical data and prior knowledge.
In the case of simulators, a key quantity required for statistical inference, the likelihood of observed data given parameters, \(\mathcal{L}(\theta) = p(x_o\theta)\), is typically intractable, rendering conventional statistical approaches inapplicable.
sbi
implements three powerful machinelearning methods that address this problem:
 Sequential Neural Posterior Estimation (SNPE),
 Sequential Neural Likelihood Estimation (SNLE), and
 Sequential Neural Ratio Estimation (SNRE).
Depending on the characteristics of the problem, e.g. the dimensionalities of the parameter space and the observation space, one of the methods will be more suitable.
Goal: Algorithmically identify mechanistic models which are consistent with data.
Each of the methods above needs three inputs: A candidate mechanistic model, prior knowledge or constraints on model parameters, and observational data (or summary statistics thereof).
The methods then proceed by
 sampling parameters from the prior followed by simulating synthetic data from these parameters,
 learning the (probabilistic) association between data (or data features) and underlying parameters, i.e. to learn statistical inference from simulated data. The way in which this association is learned differs between the above methods, but all use deep neural networks.
 This learned neural network is then applied to empirical data to derive the full space of parameters consistent with the data and the prior, i.e. the posterior distribution. High posterior probability is assigned to parameters which are consistent with both the data and the prior, low probability to inconsistent parameters. While SNPE directly learns the posterior distribution, SNLE and SNRE need an extra MCMC sampling step to construct a posterior.
 If needed, an initial estimate of the posterior can be used to adaptively generate additional informative simulations.
Publications¶
See Cranmer, Brehmer, Louppe (2020) for a recent review on simulationbased inference.
The following papers offer additional details on the inference methods included in
sbi
:
SNPE¶

Fast εfree Inference of Simulation Models with Bayesian Conditional Density Estimation
by Papamakarios & Murray (NeurIPS 2016)
[PDF] [BibTeX] 
Flexible statistical inference for mechanistic models of neural dynamics
by Lueckmann, Goncalves, Bassetto, Öcal, Nonnenmacher & Macke (NeurIPS 2017)
[PDF] [BibTeX] 
Automatic posterior transformation for likelihoodfree inference
by Greenberg, Nonnenmacher & Macke (ICML 2019)
[PDF] [BibTeX]
SNLE¶
 Sequential neural likelihood: Fast likelihoodfree inference with autoregressive flows
by Papamakarios, Sterratt & Murray (AISTATS 2019)
[PDF] [BibTeX]