Search Results for author:
Found 9 papers, 3 papers with code
Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors
In this work, we take a different approach and utilize the specific structure of the DDM prior to define a set of intermediate and simpler posterior sampling problems, resulting in a lower approximation error compared to previous methods.
Importance sampling for online variational learning
This article addresses online variational estimation in state-space models.
Online Variational Sequential Monte Carlo
Being the most classical generative model for serial data, state-space models (SSM) are fundamental in AI and statistical machine learning.
State and parameter learning with PaRIS particle Gibbs
The particle-based, rapid incremental smoother PaRIS is a sequential Monte Carlo (SMC) technique allowing for efficient online approximation of expectations of additive functionals under the smoothing distribution in these models.
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
This method is a wrapper in the sense that it uses the same proposal samples and importance weights as SNIS, but makes clever use of iterated sampling--importance resampling (ISIR) to form a bias-reduced version of the estimator.
Probabilistic feature extraction, dose statistic prediction and dose mimicking for automated radiation therapy treatment planning
We show that the features extracted by the variational autoencoder capture geometric information of substantial relevance to the dose statistic prediction problem and are related to dose statistics in a more regularized fashion than hand-crafted features.
A similarity-based Bayesian mixture-of-experts model
We present a new nonparametric mixture-of-experts model for multivariate regression problems, inspired by the probabilistic k-nearest neighbors algorithm.
Sequential sampling of junction trees for decomposable graphs
On the other hand, the junction-tree collapser provides a complementary operation for removing vertices in the underlying decomposable graph of a junction tree, while maintaining the junction tree property.
Statistics Theory Discrete Mathematics Combinatorics Statistics Theory
Bayesian inference in decomposable graphical models using sequential Monte Carlo methods
The theoretical properties of the algorithm are investigated, showing in particular that the refreshment step improves the algorithm performance in terms of asymptotic variance of the estimated distribution.
Statistics Theory Statistics Theory
