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We introduce two synthetic likelihood methods for Simulation-Based Inference (SBI), to conduct either amortized or targeted inference from experimental observations when a high-fidelity simulator is available.
Analytical note on certain rhythmic relations in organic systems
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Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons
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2.4 Statistical Decision Theory , pp. 18
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The frontier of simulation-based inference
Cranmer, K., Brehmer, J., and Louppe, G · 2020
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Your classifier is secretly an energy based model and you should treat it like one
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Likelihood-free MCMC with amortized approximate ratio estimators
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Reconstruction and simulation of neocortical microcircuitry
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Noisy monte carlo: Convergence of markov chains with approximate transition kernels
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Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology
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Sliced score matching: A scalable approach to density and score estimation
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Blindness of score-based methods to isolated components and mixing proportions
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Disparate energy consumption despite similar network activity
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Variational methods for simulation-based inference
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Recordings from the c. borealis stomatogastric nervous system at different temperatures in the decentralized condition
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Benchmarking simulation-based inference
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A tale of two flows: Cooperative learning of langevin flow and normalizing flow toward energy-based model
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Score matched neural exponential families for likelihood-free inference
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