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EbookBell Team
4.0
46 reviewsAuthor: Francisco Carrillo Perez, Marija Pizurica, Kathleen Marchal, Olivier Gevaert
Synthetic Multimodal Data Modelling for Data Imputation focuses on advanced computational and statistical methods for handling missing data through synthetic data generation and multimodal learning. The book explores theory and practical approaches for integrating heterogeneous data sources, probabilistic modelling, machine learning techniques, and validation strategies. It is intended for researchers, data scientists, and graduate students working in biomedical informatics, artificial intelligence, and data-driven modelling where incomplete datasets are a critical challenge.
synthetic multimodal data modelling
data imputation synthetic data
multimodal data imputation methods
machine learning missing data
generative models for data imputation
Tags: Synthetic, multimodal, data imputation, Francisco Carrillo Perez, Marija Pizurica, Kathleen Marchal, Olivier Gevaert
