Bayesian Inference

611 papers with code • 1 benchmarks • 7 datasets

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors

timrudner/group-aware-priors 14 Mar 2024

Machine learning models often perform poorly under subpopulation shifts in the data distribution.

0
14 Mar 2024

Scalable Spatiotemporal Prediction with Bayesian Neural Fields

google/bayesnf 12 Mar 2024

Spatiotemporal datasets, which consist of spatially-referenced time series, are ubiquitous in many scientific and business-intelligence applications, such as air pollution monitoring, disease tracking, and cloud-demand forecasting.

13
12 Mar 2024

Listening to the Noise: Blind Denoising with Gibbs Diffusion

rubenohana/gibbs-diffusion 29 Feb 2024

Assuming arbitrary parametric Gaussian noise, we develop a Gibbs algorithm that alternates sampling steps from a conditional diffusion model trained to map the signal prior to the family of noise distributions, and a Monte Carlo sampler to infer the noise parameters.

12
29 Feb 2024

Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning

probcomp/clips.jl 27 Feb 2024

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.

4
27 Feb 2024

Sequential transport maps using SoS density estimation and $α$-divergences

benjione/sequentialmeasuretransport.jl 27 Feb 2024

Transport-based density estimation methods are receiving growing interest because of their ability to efficiently generate samples from the approximated density.

1
27 Feb 2024

Stochastic Approximation with Biased MCMC for Expectation Maximization

red-portal/mcmcsaem.jl 27 Feb 2024

In practice, MCMC-SAEM is often run with asymptotically biased MCMC, for which the consequences are theoretically less understood.

0
27 Feb 2024

BlackJAX: Composable Bayesian inference in JAX

blackjax-devs/blackjax 16 Feb 2024

BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation.

701
16 Feb 2024

Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning

ssi-research/bayesagg_mtl 6 Feb 2024

Running a dedicated model for each task is computationally expensive and therefore there is a great interest in multi-task learning (MTL).

2
06 Feb 2024

Diffusive Gibbs Sampling

Wenlin-Chen/DiGS 5 Feb 2024

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.

1
05 Feb 2024

Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers

sisl/distributed_admm_sampler 29 Jan 2024

Many machine learning applications require operating on a spatially distributed dataset.

1
29 Jan 2024