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
Analytical Approximation of the ELBO Gradient in the Context of the Clutter Problem
We propose an analytical solution for approximating the gradient of the Evidence Lower Bound (ELBO) in variational inference problems where the statistical model is a Bayesian network consisting of observations drawn from a mixture of a Gaussian distribution embedded in unrelated clutter, known as the clutter problem.
All-in-one simulation-based inference
Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data.
Probabilistic Survival Analysis by Approximate Bayesian Inference of Neural Networks
thecml/baysurv
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In this paper, we study the benefits of modeling uncertainty in deep neural networks for survival analysis with a focus on prediction and calibration performance.
Predictive, scalable and interpretable knowledge tracing on structured domains
mlcolab/psi-kt
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This requires estimates of both the learner's progress (''knowledge tracing''; KT), and the prerequisite structure of the learning domain (''knowledge mapping'').
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors
Machine learning models often perform poorly under subpopulation shifts in the data distribution.
Scalable Spatiotemporal Prediction with Bayesian Neural Fields
google/bayesnf
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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.
Listening to the Noise: Blind Denoising with Gibbs Diffusion
rubenohana/gibbs-diffusion
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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.
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.
