96 citations · 148 across the 4 of their papers we have counts for
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Truncated proposals for scalable and hassle-free simulation-based inference
Michael Deistler, Pedro J Goncalves, Jakob H Macke
Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a stochastic simulator and inferring posterior distributions from model-simulations. To i…
GATSBI: Generative Adversarial Training for Simulation-Based Inference
Poornima Ramesh, Jan-Matthis Lueckmann, Jan Boelts +4
Simulation-based inference (SBI) refers to statistical inference on stochastic models for which we can generate samples, but not compute likelihoods. Like SBI algorithms, generativ…
Benchmarking Simulation-Based Inference
Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg +2
Recent advances in probabilistic modelling have led to a large number of simulation-based inference algorithms which do not require numerical evaluation of likelihoods. However, a…
Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann, Pedro J. Goncalves, Giacomo Bassetto +3
Mechanistic models of single-neuron dynamics have been extensively studied in computational neuroscience. However, identifying which models can quantitatively reproduce empirically…