Tensor decomposition: Basics, algorithms, and recent advances.- Bayesian learning for sparsity-aware modeling.- Bayesian tensor CPD: Modeling and inference.- Bayesian tensor CPD: Performance and real-world applications.- When stochastic optimization meets VI: Scaling Bayesian CPD to massive data.- Bayesian tensor CPD with nonnegative factors.- Complex-valued CPD, orthogonality constraint and beyond Gaussian noises.- Handling missing value: A case study in direction-of-arrival estimation.- From CPD to other tensor decompositions.