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Found 5 papers, 1 papers with code
A variational neural Bayes framework for inference on intractable posterior distributions
In this paper, we propose a framework for Bayesian posterior estimation by mapping data to posteriors of parameters using a neural network trained on data simulated from the complex model.
Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods
We conclude that our method provides fast and accurate parameter estimation with a reliable method of uncertainty quantification in situations where standard methods are either undesirably slow or inaccurate.
Towards black-box parameter estimation
Deep learning algorithms have recently shown to be a successful tool in estimating parameters of statistical models for which simulation is easy, but likelihood computation is challenging.
Neural Networks for Parameter Estimation in Intractable Models
We propose to use deep learning to estimate parameters in statistical models when standard likelihood estimation methods are computationally infeasible.
Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE
We employ the neural networks to approximate reconstruction maps for model parameter estimation from observational data, where the data comes from the solution of the ODE and takes the form of a time series representing dynamically spiking membrane potential of a biological neuron.
