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
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
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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
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In experiments, for both highly and weakly informative prior dynamics, our methods substantially improve upon both CSMC and sophisticated 'classical' MCMC approaches.
Mechanical constraints and cell cycle regulation in models of collective cell migration
Finally, we compare our mathematical model predictions to different experiments studying cell cycle regulation and present a quantitative analysis on the impact of mechanical constraints 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.
Tractable Function-Space Variational Inference in Bayesian Neural Networks
timrudner/fsvi
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Recognizing that the primary object of interest in most settings is the distribution over functions induced by the posterior distribution over neural network parameters, we frame Bayesian inference in neural networks explicitly as inferring a posterior distribution over functions and propose a scalable function-space variational inference method that allows incorporating prior information and results in reliable predictive uncertainty estimates.
