Bayesian Inference
623 papers with code • 1 benchmarks • 7 datasets
Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).
Libraries
Use these libraries to find Bayesian Inference models and implementationsDatasets
CIFAR-100
WebKB
UCLA Aerial Event Dataset
ExBAN
Latest papers with no code
Variational Bayesian surrogate modelling with application to robust design optimisation
The assumed prior probability density of the surrogate is a Gaussian process.
Uncertainty in latent representations of variational autoencoders optimized for visual tasks
Deep learning methods are increasingly becoming instrumental as modeling tools in computational neuroscience, employing optimality principles to build bridges between neural responses and perception or behavior.
BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models
Large language models primarily rely on inductive reasoning for decision making.
Neural Methods for Amortised Parameter Inference
Simulation-based methods for making statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements.
Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference
We consider structural vector autoregressions identified through stochastic volatility.
Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning
We provide several examples from SciML involving noisy data and \textit{epistemic uncertainty} to illustrate the potential advantages of our approach.
Bayesian Federated Model Compression for Communication and Computation Efficiency
We propose a decentralized Turbo variational Bayesian inference (D-Turbo-VBI) FL framework where we firstly propose a hierarchical sparse prior to promote a clustered sparse structure in the weight matrix.
Efficient Sound Field Reconstruction with Conditional Invertible Neural Networks
In this study, we introduce a method for estimating sound fields in reverberant environments using a conditional invertible neural network (CINN).
Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object Measurements
The robot pipeline integrates with a logging module and an online database of objects, containing over 24, 000 measurements of 63 objects with different grippers.
Bayesian Inference for Consistent Predictions in Overparameterized Nonlinear Regression
While recent theoretical studies have shed light on this behavior in linear models or nonlinear classifiers, a comprehensive understanding of overparameterization in nonlinear regression remains lacking.
