Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation

By George Papamakarios, Iain Murray. Neural Information Processing Systems, 2016

472 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5202a43d22de63139ee2bb6802bedc738dfc9235

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Citations in the corpus

Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale (2019, unknown): “Inference in simulator-based models remains a challenging problem with potential impact across many disciplines [14, 60].”
Automatic Posterior Transformation for Likelihood-Free Inference (2019, methodology): “On the M/G/1 model (Papamakarios & Murray, 2016), APT outperformed SNPE-A/B and approached SNL in performance (Fig.”
BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks (2020, methodology): “Along SMC-MMD, we apply three recent methods for neural density estimation, SNPE-A [37], SNPE-B [30], and SNPE-C ([16], also dubbed APT).”
sbi: A toolkit for simulation-based inference (2020, unknown): “More recently, algorithms based on neural networks have been developed (Greenberg, Nonnenmacher, & Macke, 2019; Hermans, Begy, & Louppe, 2020; Lueckmann et al., 2017; Papamakarios & Murray, 2016; Papamakarios, Sterratt, & Murray, 2019).”
Transformers Can Do Bayesian Inference (2021, background): “One more line of research to be mentioned is amortized simulation-based inference (Cranmer et al., 2020; Chan et al., 2018), in particular neural posterior estimation (Lueckmann et al., 2021; Papamakarios & Murray, 2016).”
Conditional Density Estimation with Bayesian Normalising Flows (2018, methodology): “Papamakarios & Murray (2016) perform stochastic variational inference in a mixture density network for an application to approximately Bayesian computation and Depeweg et al. (2017) and coauthors use stochastic inputs to a Bayesian neural network for an application to reinforcement learning.”
Analyzing Inverse Problems with Invertible Neural Networks (2018, background): “Neural networks can be trained to predict accurate sufficient statistics for parametric posteriors [30, 39], or can be designed to learn a mean-field distribution for the network’s weights via dropout variational inference [10, 24].”
Likelihood-free MCMC with Amortized Approximate Ratio Estimators (2019, result): “As such, we provide this table for historic reasons to comply with previous studies such as Papamakarios & Murray (2016), Boelts et al. (2019) and Greenberg et al. (2019). a detector response x o .”
Variational Implicit Processes (2018, methodology): “[77] George Papamakarios and Iain Murray.”
Sequential Neural Methods for Likelihood-free Inference (2018, methodology): “We follow the experimental setup of Papamakarios and Murray [17].”
Autoregressive Energy Machines (2019, methodology): “Work in progress. models have seen use as surrogate likelihoods (Papamakar-ios et al., 2019) and approximate posterior distributions (Papamakarios & Murray, 2016; Lueckmann et al., 2017) for likelihood-free inference.”
Hierarchical Implicit Models and Likelihood-Free Variational Inference (2017, methodology): “We analyze the Lotka-Volterra simulator of § 2 and follow the same setup and hyperparameters of Papamakarios and Murray [38].”
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows (2018, methodology): “Sequential Neural Posterior Estimation is a strategy for reducing the number of simulations needed by conditional neural density estimation, originally proposed by Papamakarios and Murray [54] and further developed by Lueckmann et al.”
Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation (2017, background): “Other ﬁelds where conditional density estimation plays a key role are time series forecasting in economics (Kalda and Siddiqui, 2013) and approximate Bayesian methods (Fan et al., 2013; Izbicki et al., 2014; Papamakarios and Murray, 2016).”