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Found 20 papers, 15 papers with code
Consistency Models for Scalable and Fast Simulation-Based Inference
Simulation-based inference (SBI) is constantly in search of more expressive algorithms for accurately inferring the parameters of complex models from noisy data.
Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference
This is a task where the propagation of surrogate uncertainty is especially relevant, because failing to account for it may lead to biased and/or overconfident estimates of the parameters of interest.
Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference
We present multimodal neural posterior estimation (MultiNPE), a method to integrate heterogeneous data from different sources in simulation-based inference with neural networks.
Sensitivity-Aware Amortized Bayesian Inference
In this work, we propose sensitivity-aware amortized Bayesian inference (SA-ABI), a multifaceted approach to efficiently integrate sensitivity analyses into simulation-based inference with neural networks.
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference
We propose a method to improve the efficiency and accuracy of amortized Bayesian inference by leveraging universal symmetries in the joint probabilistic model of parameters and data.
Simulation-Based Prior Knowledge Elicitation for Parametric Bayesian Models
Our results support the claim that our method is largely independent of the underlying model structure and adaptable to various elicitation techniques, including quantile-based, moment-based, and histogram-based methods.
BayesFlow: Amortized Bayesian Workflows With Neural Networks
Modern Bayesian inference involves a mixture of computational techniques for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows for data analysis.
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models
This work proposes ``jointly amortized neural approximation'' (JANA) of intractable likelihood functions and posterior densities arising in Bayesian surrogate modeling and simulation-based inference.
A Deep Learning Method for Comparing Bayesian Hierarchical Models
Bayesian model comparison (BMC) offers a principled approach for assessing the relative merits of competing computational models and propagating uncertainty into model selection decisions.
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models
Our results show that the deep learning approach is very efficient in capturing the temporal dynamics of the model.
Meta-Uncertainty in Bayesian Model Comparison
Bayesian model comparison (BMC) offers a principled probabilistic approach to study and rank competing models.
A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms
Polynomial chaos expansion (PCE) is a versatile tool widely used in uncertainty quantification and machine learning, but its successful application depends strongly on the accuracy and reliability of the resulting PCE-based response surface.
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks
Neural density estimators have proven remarkably powerful in performing efficient simulation-based Bayesian inference in various research domains.
Projection Predictive Inference for Generalized Linear and Additive Multilevel Models
Projection predictive inference is a decision theoretic Bayesian approach that decouples model estimation from decision making.
Methodology Computation
Amortized Bayesian Inference for Models of Cognition
As models of cognition grow in complexity and number of parameters, Bayesian inference with standard methods can become intractable, especially when the data-generating model is of unknown analytic form.
Group Heterogeneity Assessment for Multilevel Models
Many data sets contain an inherent multilevel structure, for example, because of repeated measurements of the same observational units.
Amortized Bayesian model comparison with evidential deep learning
This makes the method particularly effective in scenarios where model fit needs to be assessed for a large number of datasets, so that per-dataset inference is practically infeasible. Finally, we propose a novel way to measure epistemic uncertainty in model comparison problems.
Implicitly Adaptive Importance Sampling
Adaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling.
Rank-normalization, folding, and localization: An improved $\widehat{R}$ for assessing convergence of MCMC
In this paper we show that the convergence diagnostic $\widehat{R}$ of Gelman and Rubin (1992) has serious flaws.
Computation Methodology
Approximate leave-future-out cross-validation for Bayesian time series models
One of the common goals of time series analysis is to use the observed series to inform predictions for future observations.
Methodology
