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Found 12 papers, 9 papers with code
All-in-one simulation-based inference
Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data.
A Practical Guide to Statistical Distances for Evaluating Generative Models in Science
To demonstrate how these distances are used in practice, we evaluate generative models from different scientific domains, namely a model of decision making and a model generating medical images.
Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations
Ordinary differential equations (ODEs) are widely used to describe dynamical systems in science, but identifying parameters that explain experimental measurements is challenging.
Amortized Bayesian Decision Making for simulation-based models
Simulation-based inference (SBI) provides a powerful framework for inferring posterior distributions of stochastic simulators in a wide range of domains.
Adversarial robustness of amortized Bayesian inference
Bayesian inference usually requires running potentially costly inference procedures separately for every new observation.
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
Generalized Bayesian Inference (GBI) aims to robustify inference for (misspecified) simulator models, replacing the likelihood-function with a cost function that evaluates the goodness of parameters relative to data.
Efficient identification of informative features in simulation-based inference
To this end, many SBI methods employ a set of summary statistics or scientifically interpretable features to estimate a surrogate likelihood or posterior.
Truncated proposals for scalable and hassle-free simulation-based inference
TSNPE performs sequential inference with truncated proposals, sidestepping the optimisation issues of alternative approaches.
Variational methods for simulation-based inference
We present Sequential Neural Variational Inference (SNVI), an approach to perform Bayesian inference in models with intractable likelihoods.
Group equivariant neural posterior estimation
We here describe an alternative method to incorporate equivariances under joint transformations of parameters and data.
SBI -- A toolkit for simulation-based inference
$\texttt{sbi}$ facilitates inference on black-box simulators for practising scientists and engineers by providing a unified interface to state-of-the-art algorithms together with documentation and tutorials.
Tactile Hallucinations on Artificial Skin Induced by Homeostasis in a Deep Boltzmann Machine
In this work, we investigate the generation of tactile hallucinations on biologically inspired, artificial skin.
