Recent advances in probabilistic deep learning enable efficient amortized Bayesian inference in settings where the likelihood function is only implicitly defined by a simulation program (simulation-based inference; SBI). But how faithful is such inference if the simulation represents reality somewhat ...
Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid amortized inference with gold-standard MCMC techniques to achieve both speed and accuracy when performing inference on many observed datasets. Our approach use...
amortized-inference Star Here are 5 public repositories matching this topic... Language: All bayesflow-org / bayesflow Star 361 Code Issues Pull requests A Python library for amortized Bayesian workflows using generative neural networks. deep-learning parameter-estimation bayesian-inference uncertainty-...
Recent advances in simulation-based inference offer promising solutions for addressing complex probabilistic models using deep generative networks. However, the utility and reliability of deep learning methods for estimating Bayesian MLMs remains largely unexplored, especially when compared with gold-standard ...
Amortized Bayesian Decision Making for Simulation-based Models This repository provides the implementation used for the paper Amortized Bayesian Decision Making for simulation-based models. For the full git commit history with correct time stamps, see the branch paper. In this work we address the ques...
Welcome to our BayesFlow library for efficient simulation-based Bayesian workflows! Our library enables users to create specialized neural networks for amortized Bayesian inference, which repay users with rapid statistical inference after a potentially longer simulation-based training phase....
Bayesian model inversion can be used to solve this problem, but typically requires either computationally expensive MCMC sampling, or faster but approximate maximum-a-posteriori optimization. Here, we introduce a flexible algorithmic framework for fast, efficient and accurate extraction of neural spikes ...
Exact inference for large, directed graphical models, also known as Bayesian networks (BNs), can be intractable as the space complexity grows exponentially in the tree-width of the model. Approximate inference, such as generalized belief propagation (GBP), is used instead. GBP tre...
BayesFlow Can Reliably Detect Model Misspecification and Posterior Errors in Amortized Bayesian Inference Neural density estimators have proven remarkably powerful in performing efficient simulation-based Bayesian inference in various research domains. In particular, the BayesFlow framework uses a two-step app...
Finally, we provide an open-source implementation of our methods to stimulate further research in the nascent field of amortized Bayesian inference. PDF Abstract Code Edit No code implementations yet. Submit your code now Tasks Edit Bayesian Inference Efficient Neural Network ...