Inference and Learning in a Latent Variable Model for Beta Distributed Interval Data

Inference and Learning in a Latent Variable Model for Beta Distributed Interval Data

Latent Variable Models (LVMs) are well established tools to accomplish a range of different data processing tasks. Applications exploit the ability of LVMs to identify latent data structure in order to improve data (e.g., through denoising) or to estimate the relation between latent causes and measu...

Full description

Bibliographic Details
Main Authors: Hamid Mousavi, Mareike Buhl, Enrico Guiraud, Jakob Drefs, Jörg Lücke
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Entropy
Subjects:
latent variable models
Bayes nets
Beta distribution
noisy-OR
expectation maximization
variational inference
Online Access:https://www.mdpi.com/1099-4300/23/5/552

Internet

https://www.mdpi.com/1099-4300/23/5/552

Similar Items