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The summary-likelihood method and its implementation in the Infusion package.
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A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation.
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Monte Carlo integration over stepping stone models for spatial genetic inference using approximate Bayesian computation.
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Model selection and parameter estimation for root architecture models using likelihood-free inference.
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Biophysics-based protein language models for protein engineering.
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Perspective: An outlook on fluorescence tracking.
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Latent variable sequence identification for cognitive models with neural network estimators.
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The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology.
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Simulation-based inference of developmental EEG maturation with the spectral graph model.
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本文引用的文献

1
Mining gold from implicit models to improve likelihood-free inference.
Proc Natl Acad Sci U S A. 2020 Mar 10;117(10):5242-5249. doi: 10.1073/pnas.1915980117. Epub 2020 Feb 20.
2
Likelihood-free inference via classification.
Stat Comput. 2018;28(2):411-425. doi: 10.1007/s11222-017-9738-6. Epub 2017 Mar 13.
3
Constraining Effective Field Theories with Machine Learning.
Phys Rev Lett. 2018 Sep 14;121(11):111801. doi: 10.1103/PhysRevLett.121.111801.
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Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions.
Neural Comput. 2018 Nov;30(11):3072-3094. doi: 10.1162/neco_a_01127. Epub 2018 Sep 14.
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Deep learning.
Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
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Sequential Monte Carlo without likelihoods.
Proc Natl Acad Sci U S A. 2007 Feb 6;104(6):1760-5. doi: 10.1073/pnas.0607208104. Epub 2007 Jan 30.
7
Markov chain Monte Carlo without likelihoods.
Proc Natl Acad Sci U S A. 2003 Dec 23;100(26):15324-8. doi: 10.1073/pnas.0306899100. Epub 2003 Dec 8.
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Approximate Bayesian computation in population genetics.
Genetics. 2002 Dec;162(4):2025-35. doi: 10.1093/genetics/162.4.2025.

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