ArticleProceedings of the National Academy of Sciences of the United States of America2020
Mining gold from implicit models to improve likelihood-free inference.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 164 citations in OpenAlex.
- Perilesional neuromodulation replaces lost sensorimotor function in persons with spinal cord injury.Nature biomedical engineering · 2026Article
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- Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference.PLoS computational biology · 2024Article
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- Generative models of morphogenesis in developmental biology.Seminars in cell & developmental biology · 2023Review
- Bagged filters for partially observed interacting systems.Journal of the American Statistical Association · 2023Article
- Fast inference of spinal neuromodulation for motor control using amortized neural networks.Journal of neural engineering · 2022Article
- Consilience of methods for phylogenetic analysis of variance.Evolution; international journal of organic evolution · 2022Article
- Learning new physics from an imperfect machine.The European physical journal. C, Particles and fields · 2022Article
- Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators.BMC bioinformatics · 2021Article
- The frontier of simulation-based inference.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Simulators often provide the best description of real-world phenomena. However, the probability density that they implicitly define is often intractable, leading to challenging inverse problems for inference. Recently, a number of techniques have been introduced in which a surrogate for the intractable density is learned, including normalizing flows and density ratio estimators. We show that additional information that characterizes the latent process can often be extracted from simulators and used to augment the training data for these surrogate models. We introduce several loss functions that leverage these augmented data and demonstrate that these techniques can improve sample efficiency and quality of inference.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.