Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …

Incomplete data are quite common in biomedical and other types of research, especially in
longitudinal studies. During the last three decades, a vast amount of work has been done in …

Quantizing images into discrete representations has been a fundamental problem in unified
generative modeling. Predominant approaches learn the discrete representation either in a …

We describe NIMBLE, a system for programming statistical algorithms for general model
structures within R. NIMBLE is designed to meet three challenges: flexible model …

Statistical guarantees for the EM algorithm: From population to sample-based analysis Page 1
The Annals of Statistics 2017, Vol. 45, No. 1, 77–120 DOI: 10.1214/16-AOS1435 © Institute of …

The organization of human auditory cortex remains unresolved, due in part to the small
stimulus sets common to fMRI studies and the overlap of neural populations within voxels …

* Contains additional discussion and examples on left truncation as well as material on more
general censoring and truncation patterns.* Introduces the martingale and counting process …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

The only single-source——now completely updated and revised——to offer a unified
treatment of the theory, methodology, and applications of the EM algorithm Complete with …

1.1 Introduction Research into time series models of changing variance and covariance,
which I will collectively call volatility models, has exploded in the last ten years. This activity …