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
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning
Our agent assists a human by modeling them as a cooperative planner who communicates joint plans to the assistant, then performs multimodal Bayesian inference over the human's goal from actions and language, using large language models (LLMs) to evaluate the likelihood of an instruction given a hypothesized plan.
Sequential transport maps using SoS density estimation and $α$-divergences
Transport-based density estimation methods are receiving growing interest because of their ability to efficiently generate samples from the approximated density.
Stochastic Approximation with Biased MCMC for Expectation Maximization
In practice, MCMC-SAEM is often run with asymptotically biased MCMC, for which the consequences are theoretically less understood.
BlackJAX: Composable Bayesian inference in JAX
BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation.
Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning
Running a dedicated model for each task is computationally expensive and therefore there is a great interest in multi-task learning (MTL).
Diffusive Gibbs Sampling
Wenlin-Chen/DiGS
•
•
The inadequate mixing of conventional Markov Chain Monte Carlo (MCMC) methods for multi-modal distributions presents a significant challenge in practical applications such as Bayesian inference and molecular dynamics.
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers
Many machine learning applications require operating on a spatially distributed dataset.
Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space models
adriencorenflos/particle_mala
•
•
In experiments, for both highly and weakly informative prior dynamics, our methods substantially improve upon both CSMC and sophisticated 'classical' MCMC approaches.
Quantifying cell cycle regulation by tissue crowding
Finally, we compare our mathematical model predictions to different experiments studying cell cycle regulation and present a quantitative analysis on the impact of density-dependent regulation on cell migration patterns.
A Compact Representation for Bayesian Neural Networks By Removing Permutation Symmetry
Bayesian neural networks (BNNs) are a principled approach to modeling predictive uncertainties in deep learning, which are important in safety-critical applications.
