Bayesian Inference
621 papers with code • 1 benchmarks • 7 datasets
Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).
Datasets
CIFAR-100
WebKB
UCLA Aerial Event Dataset
ExBAN
Latest papers with no code
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.
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation
Model-based RL, by building a dynamic model of the robot, enables data reuse and transfer learning between tasks with the same robot and similar environment.
Accounting for contact network uncertainty in epidemic inferences
However, in realistic settings, the observed data often serves as an imperfect proxy of the actual contact patterns in the population.
A Unified Kernel for Neural Network Learning
Two predominant approaches have emerged: the Neural Network Gaussian Process (NNGP) and the Neural Tangent Kernel (NTK).
Divide, Conquer, Combine Bayesian Decision Tree Sampling
A challenge for existing MCMC approaches is proposing joint changes in both the tree structure and the decision parameters that result in efficient sampling.
