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20230928193704 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 09 + 2023 + + + 8 + + 89 + + + + BlackBIRDS: Black-Box Inference foR Differentiable +Simulators + + + + Arnau + Quera-Bofarull + https://orcid.org/0000-0001-5055-9863 + + + Joel + Dyer + https://orcid.org/0000-0002-8304-8450 + + + Anisoara + Calinescu + https://orcid.org/0000-0003-2082-734X + + + J. Doyne + Farmer + https://orcid.org/0000-0001-7871-073X + + + Michael + Wooldridge + https://orcid.org/0000-0002-9329-8410 + + + + 09 + 28 + 2023 + + + 5776 + + + 10.21105/joss.05776 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.8377044 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5776 + + + + 10.21105/joss.05776 + https://joss.theoj.org/papers/10.21105/joss.05776 + + + https://joss.theoj.org/papers/10.21105/joss.05776.pdf + + + + + + PyTorch: An imperative style, +high-performance deep learning library + Paszke + Proceedings of the 33rd international +conference on neural information processing systems + 2019 + Paszke, A., Gross, S., Massa, F., +Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, +N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, +M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., … Chintala, S. +(2019). PyTorch: An imperative style, high-performance deep learning +library. In Proceedings of the 33rd international conference on neural +information processing systems. Curran Associates +Inc. + + + Normflows: A PyTorch package for normalizing +flows + Stimper + Journal of Open Source +Software + 86 + 8 + 10.21105/joss.05361 + 2023 + Stimper, V., Liu, D., Campbell, A., +Berenz, V., Ryll, L., Schölkopf, B., & Hernández-Lobato, J. M. +(2023). Normflows: A PyTorch package for normalizing flows. Journal of +Open Source Software, 8(86), 5361. +https://doi.org/10.21105/joss.05361 + + + Differentiable agent-based +epidemiology + Chopra + Proceedings of the 2023 international +conference on autonomous agents and multiagent systems + 978-1-4503-9432-1 + 2023 + Chopra, A., Rodríguez, A., +Subramanian, J., Quera-Bofarull, A., Krishnamurthy, B., Prakash, B. A., +& Raskar, R. (2023). Differentiable agent-based epidemiology. +Proceedings of the 2023 International Conference on Autonomous Agents +and Multiagent Systems, 1848–1857. +ISBN: 978-1-4503-9432-1 + + + Differentiable programming in high-energy +physics + Baydin + 2020 + Baydin, A. G., NYU, K. C., Feickert, +M., Gray, L., Heinrich, L., NYU, A. H., Neubauer, A. M. V. M., Pearkes, +J., Simpson, N., Smith, N., & others. (2020). Differentiable +programming in high-energy physics. + + + Indirect inference + Gourieroux + Journal of applied +econometrics + S1 + 8 + 1993 + Gourieroux, C., Monfort, A., & +Renault, E. (1993). Indirect inference. Journal of Applied Econometrics, +8(S1), S85–S118. + + + Applying the method of simulated moments to +estimate a small agent-based asset pricing model + Franke + Journal of Empirical Finance + 5 + 16 + 10.1016/j.jempfin.2009.06.006 + 2009 + Franke, R. (2009). Applying the +method of simulated moments to estimate a small agent-based asset +pricing model. Journal of Empirical Finance, 16(5), 804–815. +https://doi.org/10.1016/j.jempfin.2009.06.006 + + + Exponential convergence of Langevin +distributions and their discrete approximations + Roberts + Bernoulli + 10.2307/3318418 + 1996 + Roberts, G. O., & Tweedie, R. L. +(1996). Exponential convergence of Langevin distributions and their +discrete approximations. Bernoulli, 341–363. +https://doi.org/10.2307/3318418 + + + Hybrid Monte Carlo + Duane + Physics letters B + 2 + 195 + 1987 + Duane, S., Kennedy, A. D., Pendleton, +B. J., & Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters B, +195(2), 216–222. + + + A general framework for updating belief +distributions + Bissiri + Journal of the Royal Statistical Society: +Series B (Statistical Methodology) + 5 + 78 + 10.1111/rssb.12158 + 2016 + Bissiri, P. G., Holmes, C., & +Walker, S. G. (2016). A general framework for updating belief +distributions. Journal of the Royal Statistical Society: Series B +(Statistical Methodology), 78(5), 1103. +https://doi.org/10.1111/rssb.12158 + + + An optimization-centric view on Bayes’ rule: +Reviewing and generalizing variational inference + Knoblauch + Journal of Machine Learning +Research + 132 + 23 + 2022 + Knoblauch, J., Jewson, J., & +Damoulas, T. (2022). An optimization-centric view on Bayes’ rule: +Reviewing and generalizing variational inference. Journal of Machine +Learning Research, 23(132), 1–109. + + + Bayesian calibration of differentiable +agent-based models + Quera-Bofarull + ICLR Workshop on AI for Agent-based +Modelling + 2023 + Quera-Bofarull, A., Chopra, A., +Calinescu, A., Wooldridge, M., & Dyer, J. (2023). Bayesian +calibration of differentiable agent-based models. ICLR Workshop on AI +for Agent-Based Modelling. + + + Some challenges of calibrating differentiable +agent-based models + Quera-Bofarull + ICML Differentiable Almost Everything +Workshop + 2023 + Quera-Bofarull, A., Dyer, J., +Calinescu, A., & Wooldridge, M. (2023). Some challenges of +calibrating differentiable agent-based models. ICML Differentiable +Almost Everything Workshop. + + + Sbi: A toolkit for simulation-based +inference + Tejero-Cantero + Journal of Open Source +Software + 52 + 5 + 10.21105/joss.02505 + 2020 + Tejero-Cantero, A., Boelts, J., +Deistler, M., Lueckmann, J.-M., Durkan, C., Gonçalves, P. J., Greenberg, +D. S., & Macke, J. H. (2020). Sbi: A toolkit for simulation-based +inference. Journal of Open Source Software, 5(52), 2505. +https://doi.org/10.21105/joss.02505 + + + Black-it: A ready-to-use and easy-to-extend +calibration kit for agent-based models + Benedetti + Journal of Open Source +Software + 79 + 7 + 10.21105/joss.04622 + 2022 + Benedetti, M., Catapano, G., Sclavis, +F. D., Favorito, M., Glielmo, A., Magnanimi, D., & Muci, A. (2022). +Black-it: A ready-to-use and easy-to-extend calibration kit for +agent-based models. Journal of Open Source Software, 7(79), 4622. +https://doi.org/10.21105/joss.04622 + + + PyVBMC: Efficient Bayesian inference in +python + Huggins + Journal of Open Source +Software + 86 + 8 + 10.21105/joss.05428 + 2023 + Huggins, B., Li, C., Tobaben, M., +Aarnos, M. J., & Acerbi, L. (2023). PyVBMC: Efficient Bayesian +inference in python. Journal of Open Source Software, 8(86), 5428. +https://doi.org/10.21105/joss.05428 + + + ABCpy: A high-performance computing +perspective to approximate Bayesian computation + Dutta + Journal of Statistical +Software + 7 + 100 + 10.18637/jss.v100.i07 + 2021 + Dutta, R., Schoengens, M., +Pacchiardi, L., Ummadisingu, A., Widmer, N., Künzli, P., Onnela, J.-P., +& Mira, A. (2021). ABCpy: A high-performance computing perspective +to approximate Bayesian computation. Journal of Statistical Software, +100(7), 1–38. +https://doi.org/10.18637/jss.v100.i07 + + + pyABC: Efficient and robust easy-to-use +approximate Bayesian computation + Schälte + Journal of Open Source +Software + 74 + 7 + 10.21105/joss.04304 + 2022 + Schälte, Y., Klinger, E., Alamoudi, +E., & Hasenauer, J. (2022). pyABC: Efficient and robust easy-to-use +approximate Bayesian computation. Journal of Open Source Software, +7(74), 4304. https://doi.org/10.21105/joss.04304 + + + BayesFlow: Amortized Bayesian Workflows With +Neural Networks + Radev + Journal of Open Source +Software + 89 + 8 + 10.21105/joss.05702 + 2023 + Radev, S. T., Schmitt, M., +Schumacher, L., Elsemüller, L., Pratz, V., Schälte, Y., Köthe, U., & +Bürkner, P.-C. (2023). BayesFlow: Amortized Bayesian Workflows With +Neural Networks. Journal of Open Source Software, 8(89), 5702. +https://doi.org/10.21105/joss.05702 + + + + + + From 6e119378cb41e3ea1e7e27596895dd5aeaa9f109 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Thu, 28 Sep 2023 20:37:28 +0100 Subject: [PATCH 3/3] Creating 10.21105.joss.05776.jats --- joss.05776/10.21105.joss.05776.jats | 756 ++++++++++++++++++++++++++++ 1 file changed, 756 insertions(+) create mode 100644 joss.05776/10.21105.joss.05776.jats diff --git a/joss.05776/10.21105.joss.05776.jats b/joss.05776/10.21105.joss.05776.jats new file mode 100644 index 0000000000..0b36d9fa77 --- /dev/null +++ b/joss.05776/10.21105.joss.05776.jats @@ -0,0 +1,756 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5776 +10.21105/joss.05776 + +BlackBIRDS: Black-Box Inference foR Differentiable +Simulators + + + +https://orcid.org/0000-0001-5055-9863 + +Quera-Bofarull +Arnau + + + +* + + +https://orcid.org/0000-0002-8304-8450 + +Dyer +Joel + + + +* + + +https://orcid.org/0000-0003-2082-734X + +Calinescu +Anisoara + + + + +https://orcid.org/0000-0001-7871-073X + +Farmer +J. Doyne + + + + + + +https://orcid.org/0000-0002-9329-8410 + +Wooldridge +Michael + + + + + +Department of Computer Science, University of Oxford, +UK + + + + +Institute for New Economic Thinking, University of Oxford, +UK + + + + +Mathematical Institute, University of Oxford, +UK + + + + +Santa Fe Institute, USA + + + + +* E-mail: +* E-mail: + + +14 +7 +2023 + +8 +89 +5776 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +Bayesian inference +differentiable simulators +variational inference +Markov chain Monte Carlo + + + + + + Summary +

BlackBIRDS is a Python package consisting of + generically applicable, black-box inference methods for differentiable + simulation models. It facilitates both (a) the differentiable + implementation of simulation models by providing a common + object-oriented framework for their implementation in + PyTorch + (Paszke + et al., 2019), and (b) the use of a variety of + gradient-assisted inference procedures for these simulation models, + allowing researchers to easily exploit the differentiable nature of + their simulator in parameter estimation tasks. The package consists of + both Bayesian and non-Bayesian inference methods, and relies on + well-supported software libraries (e.g., + normflows, + Stimper + et al., 2023) to provide this broad functionality.

+
+ + Statement of need +

Across scientific disciplines and application domains, simulation + is used extensively as a means to studying complex mathematical models + of real-world systems. A simulation-based approach to modelling such + systems provides the modeller with significant benefits, permitting + them to specify their model in the way that they believe most + faithfully represents the true data-generating process and relieving + them from concerns regarding the mathematical tractability of the + resulting model. However, this additional flexibility comes at a + price: the resulting model can be too complex to easily perform + optimisation and inference tasks on the corresponding simulator, which + in many cases necessitates the use of approximate, simulation-based + inference and optimisation methods to perform these tasks + inexactly.

+

The complicated and black-box nature of many simulation models can + present a significant barrier to the successful deployment of these + simulation-based inference and optimisation techniques. Consequently, + there has been increasing interest within various scientific + communities in constructing differentiable simulation + models (see e.g., + Baydin + et al., 2020; + Chopra + et al., 2023): simulation models for which the gradient of the + model output with respect to the model’s input parameters can be + easily obtained. The primary motivation for this is that access to + this additional information, which captures the sensitivity of the + output of the simulator to changes in the input, can enable the use of + more efficient simulation-based optimisation and inference procedures, + helping to reduce the total runtime of such algorithms, their overall + consumption of valuable computational resources, and their concomitant + financial and environmental costs.

+

To this end, BlackBIRDS was designed to + provide researchers with easy access to a set of parameter inference + methods that exploit the gradient information provided by + differentiable simulators. The package provides support for a variety + of approaches to gradient-assisted parameter inference, including:

+ + +

Simulated Minimum Distance + (Franke, + 2009; SMD, see e.g., + Gourieroux + et al., 1993), in which parameter point estimates + + + 𝛉̂ + are obtained as

+

+ + 𝛉̂=argmin𝛉𝚯(𝛉,𝐲), + where + + 𝛉 + are the simulator’s parameters, which take values in some set + + + 𝚯, + and + + + is a loss function capturing the compatibility between the + observed data + + 𝐲 + and the simulator’s behaviour at parameter vector + + + 𝛉;

+
+ +

Markov chain Monte Carlo (MCMC), in which samples from a + parameter posterior

+

+ + π(𝛉𝐲)e(𝛉,𝐲)π(𝛉),

+

corresponding to a choice of loss function + + + + and a prior density + + π + over + + 𝚯 + are generated by executing a Markov chain on + + + 𝚯. + Currently, support is provided for Metropolis-adjusted Langevin + Dynamics + (Roberts + & Tweedie, 1996), although nothing prevents the + inclusion of additional gradient-assisted MCMC algorithms such as + Hamiltonian Monte Carlo + (Duane + et al., 1987);

+
+ +

Variational Inference (VI), in which a parameteric + approximation + + q* + to the intractable posterior is obtained by solving the following + optimisation problem over a variational family + + + 𝒬 + + + q*=argminq𝒬𝔼q[(𝛉,𝐲)]+𝔼q[logq(𝛉)π(𝛉)], + where + + + is defined as above and + + π + is a prior density over + + 𝚯.

+
+
+

The package is written such that the user is free to specify their + choice of + + + and + + π + (in the case of Bayesian methods), under the constraint that both + choices are differentiable with respect to + + + 𝛉. + This allows the user to target a wide variety of parameter point + estimators, and both classical and generalised (see e.g., + Bissiri + et al., 2016; + Knoblauch + et al., 2022) posteriors. We provide a number of + tutorials + demonstrating (a) how to implement a simulator in a differentiable + framework in PyTorch and (b) how to apply the different parameter + inference methods supported by BlackBIRDS to + these differentiable simulators. Our package provides the user with + flexible posterior density estimators with the use of normalising + flows, and has already been used in scientific research to calibrate + differentiable simulators + (Quera-Bofarull, + Chopra, et al., 2023; + Quera-Bofarull, + Dyer, et al., 2023).

+ + Related software +

BlackBIRDS offers complementary + functionality to a number of existing Python packages. + sbi + (Tejero-Cantero + et al., 2020) is a package offering PyTorch-based + implementations of numerous simulation-based inference algorithms, + including those based on the use of MCMC and neural conditional + density estimators. Our package differs significantly, however: in + contrast to sbi, + BlackBIRDS provides support for both Bayesian + and non-Bayesian inference methods, and permits the researcher to + exploit gradients of the simulator, loss function, and/or posterior + density with respect to parameters + + 𝛉 + during inference tasks. The same comparison applies to the the + BayesFlow package + (Radev + et al., 2023). black-it + (Benedetti + et al., 2022) is a further recent Python package that + collects some recently developed parameter estimation methods from + the agent-based modelling community; the focus of this package is, + however, on non-Bayesian methods, and the package does not currently + support the exploitation of simulator gradients. + PyVBMC + (Huggins + et al., 2023) provides a Python implementation of the + Variational Bayesian Monte Carlo algorithm using Gaussian processes, + but differs from our package in that it does not exploit simulator + gradients and is focused on Bayesian inference alone. Additional + older packages (e.g., + Dutta + et al., 2021; + Schälte + et al., 2022) also focus on approximate Bayesian inference + methods for non-differentiable simulators. Beyond this, we are + unaware of other mature software projects in Python that support + parameter inference in the specific case of differentiable + simulation models.

+
+
+ + Features + + +

User-friendly and flexible API: SMD only requires the loss + function + + + and the optimiser to use, while MCMC (resp. VI) requires only the + loss + + , + the prior density + + π, + and the MCMC method (resp. posterior approximator) to be + specified. However, additional arguments can be provided to + straightforwardly customise hyperparameters of the different + methods.

+
+ +

Multi-GPU parallelisation support with MPI.

+
+ +

Support for both forward-mode and reverse-mode + auto-differentiation.

+
+ +

Continuous integration and unit tests.

+
+
+
+ + Acknowledgements +

This research was supported by a UKRI AI World Leading Researcher + Fellowship awarded to Wooldridge (grant EP/W002949/1). M. Wooldridge + and A. Calinescu acknowledge funding from Trustworthy AI - Integrating + Learning, Optimisation and Reasoning + (TAILOR), + a project funded by European Union Horizon2020 research and innovation + program under Grant Agreement 952215.

+
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