BALM
Cross-source consensus on BALM from 1 sources and 4 claims.
1 sources · 4 claims
Uses
How it works
Benefits
Highlighted claims
- BALM combines continuous mixed membership, low-rank latent score templates, and a hurdle likelihood for zero-inflated connectomes. — A Bayesian Adaptive Latent Mixture Model for Zero-Inflated Weighted Brain Connectome Analysis
- BALM is designed to learn population-level connectivity motifs and quantify each subject's degree of expression of those motifs. — A Bayesian Adaptive Latent Mixture Model for Zero-Inflated Weighted Brain Connectome Analysis
- BALM gives posterior uncertainty for topology, weights, sparsity coupling, templates, and subject-specific mixture weights. — A Bayesian Adaptive Latent Mixture Model for Zero-Inflated Weighted Brain Connectome Analysis
- BALM is applicable beyond connectomics to other sparse weighted replicated networks. — A Bayesian Adaptive Latent Mixture Model for Zero-Inflated Weighted Brain Connectome Analysis