Publication

MAP Estimation for Bayesian Mixture Models with Submodular Priors

Volkan Cevher, Luca Baldassarre, Marwa El Halabi
2014
Conference Papers

Abstract

We propose a Bayesian approach where the signal structure can be represented by a mixture model with a submodular prior. We consider an observation model that leads to Lipschitz functions. Due to its combinatorial nature, computing the maximum a posteriori estimate for this model is NP-Hard, nonetheless our converging majorization-minimization scheme yields approximate estimates that, in practice, outperform state-of-the-art methods.

Official source

https://infoscience.epfl.ch/entities/publication/e70ad2c1-085f-4ef3-9c98-1fed7b8f916f

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

MAP Estimation for Bayesian Mixture Models with Submodular Priors

Chat with Graph Search

Deciphering the nature of cell state transitions in single cells using quantitative modeling of temporal dynamics

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Stochastic pairwise preference convergence in Bayesian agents