Corpus ID: 231717221

Simulation-Based Inference with Approximately Correct Parameters via Maximum Entropy

@inproceedings{Barrett2020SimulationBasedIW,
  title={Simulation-Based Inference with Approximately Correct Parameters via Maximum Entropy},
  author={Rainier Barrett and Mehrad Gholizadeh Ansari and Gourab Ghoshal and Andrew D White},
  year={2020}
}
Inferring the input parameters of simulators from observations is a crucial challenge with applications from epidemiology to molecular dynamics. Here we show a simple approach in the regime of sparse data and approximately correct models, which is common when trying to use an existing model to infer latent variables with observed data. This approach is based on the principle of maximum entropy and provably makes the smallest change in the latent joint distribution to accommodate new data. This… Expand

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