Then, an extension to general inverse problems is achieved through a latent variable formulation. Two expectation-maximization (EM) algorithms are then proposed to estimate the signal together with ...

Intelligent systems could be increasingly powerful by applying probabilistic inferences over the dependence relations among observed and latent variables, which could be represented by the Bayesian ...

Hierarchial Latent Variable Models for Neural Data Analysis This repository provides a fully reproducible pipeline for performing Probabilistic Canonical Correlation Analysis (PCCA) on neural data.

An online clustering algorithm predicting model for prostate cancer based on PHI-related variables and PI-RADS in different PSA populations.. If you have the appropriate software installed, you can ...

ABSTRACT Objectives: To identify in the scientific literature the prevalence, diagnostic methods, and exposure variables of latent infection by Mycobacterium tuberculosis in healthcare workers.

Article citations More>> Wang, M. (2014) Latent Variable Modeling and Mplus Applications—Fundamentals. Chongqing University Press. has been cited by the following article: TITLE: Latent Categories of ...

Article citations More>> Wang, M. (2018) Latent Variable Modeling and Mplus Applications—Advanced Chapter. Chongqing University Press. has been cited by the following article: TITLE: Latent Categories ...

Learn what latent variable models are, how they work, and what are some of the latest advances and applications of latent variable models for generative AI.

We find that populations coupled to multiple latent variables produce critical behavior across a broader parameter range than those coupled to a single, quasi-static latent variable, but in both cases ...