Bayesian Inference
624 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
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.
Bridging the Sim-to-Real Gap with Bayesian Inference
We present SIM-FSVGD for learning robot dynamics from data.
Clustered Mallows Model
For a number of reasons, strict preferences can be unrealistic assumptions for real data.
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings
Bayesian inference for complex models with an intractable likelihood can be tackled using algorithms performing many calls to computer simulators.
In-context Exploration-Exploitation for Reinforcement Learning
In-context learning is a promising approach for online policy learning of offline reinforcement learning (RL) methods, which can be achieved at inference time without gradient optimization.
