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https://doi.org/10.21256/zhaw-25627| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Learning summary statistics for Bayesian inference with autoencoders |
| Authors: | Albert, Carlo Ulzega, Simone Ozdemir, Firat Perez-Cruz, Fernando Mira, Antonietta |
| et. al: | No |
| DOI: | 10.21468/SciPostPhysCore.5.3.043 10.21256/zhaw-25627 |
| Published in: | SciPost Physics Core |
| Volume(Issue): | 5 |
| Issue: | 3 |
| Page(s): | 043 |
| Issue Date: | 2022 |
| Publisher / Ed. Institution: | SciPost Foundation |
| ISSN: | 2666-9366 |
| Language: | English |
| Subjects: | Bayesian inference; Machine learning; Deep neural network; Autoencoder; Dimensionality reduction |
| Subject (DDC): | 006: Special computer methods 510: Mathematics |
| Abstract: | For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of observations with simulated model outputs in terms of a small set of summary statistics. These statistics need to retain the information that is relevant for constraining the parameters but cancel out the noise. They can thus be seen as thermodynamic state variables, for general stochastic models. For many scientific applications, we need strictly more summary statistics than model parameters to reach a satisfactory approximation of the posterior. Therefore, we propose to use a latent representation of deep neural networks based on Autoencoders as summary statistics. To create an incentive for the encoder to encode all the parameter-related information but not the noise, we give the decoder access to explicit or implicit information on the noise that has been used to generate the training data. We validate the approach empirically on two types of stochastic models. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/25627 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | Life Sciences and Facility Management |
| Organisational Unit: | Institute of Computational Life Sciences (ICLS) |
| Published as part of the ZHAW project: | BISTOM - Bayesian Inference with Stochastic Models |
| Appears in collections: | Publikationen Life Sciences und Facility Management |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 2022_Albert-etal_Learning-summary-statistics-Bayesian-inference-autoencoders.pdf | 4.57 MB | Adobe PDF |  View/Open |
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Albert, C., Ulzega, S., Ozdemir, F., Perez-Cruz, F., & Mira, A. (2022). Learning summary statistics for Bayesian inference with autoencoders. SciPost Physics Core, 5(3), 43. https://doi.org/10.21468/SciPostPhysCore.5.3.043
Albert, C. et al. (2022) ‘Learning summary statistics for Bayesian inference with autoencoders’, SciPost Physics Core, 5(3), p. 043. Available at: https://doi.org/10.21468/SciPostPhysCore.5.3.043.
C. Albert, S. Ulzega, F. Ozdemir, F. Perez-Cruz, and A. Mira, “Learning summary statistics for Bayesian inference with autoencoders,” SciPost Physics Core, vol. 5, no. 3, p. 043, 2022, doi: 10.21468/SciPostPhysCore.5.3.043.
ALBERT, Carlo, Simone ULZEGA, Firat OZDEMIR, Fernando PEREZ-CRUZ und Antonietta MIRA, 2022. Learning summary statistics for Bayesian inference with autoencoders. SciPost Physics Core. 2022. Bd. 5, Nr. 3, S. 043. DOI 10.21468/SciPostPhysCore.5.3.043
Albert, Carlo, Simone Ulzega, Firat Ozdemir, Fernando Perez-Cruz, and Antonietta Mira. 2022. “Learning Summary Statistics for Bayesian Inference with Autoencoders.” SciPost Physics Core 5 (3): 43. https://doi.org/10.21468/SciPostPhysCore.5.3.043.
Albert, Carlo, et al. “Learning Summary Statistics for Bayesian Inference with Autoencoders.” SciPost Physics Core, vol. 5, no. 3, 2022, p. 43, https://doi.org/10.21468/SciPostPhysCore.5.3.043.
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